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      Breath Analysis as a Potential and Non-Invasive Frontier in Disease Diagnosis: An Overview

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          Abstract

          Currently, a small number of diseases, particularly cardiovascular (CVDs), oncologic (ODs), neurodegenerative (NDDs), chronic respiratory diseases, as well as diabetes, form a severe burden to most of the countries worldwide. Hence, there is an urgent need for development of efficient diagnostic tools, particularly those enabling reliable detection of diseases, at their early stages, preferably using non-invasive approaches. Breath analysis is a non-invasive approach relying only on the characterisation of volatile composition of the exhaled breath (EB) that in turn reflects the volatile composition of the bloodstream and airways and therefore the status and condition of the whole organism metabolism. Advanced sampling procedures (solid-phase and needle traps microextraction) coupled with modern analytical technologies (proton transfer reaction mass spectrometry, selected ion flow tube mass spectrometry, ion mobility spectrometry, e-noses, etc.) allow the characterisation of EB composition to an unprecedented level. However, a key challenge in EB analysis is the proper statistical analysis and interpretation of the large and heterogeneous datasets obtained from EB research. There is no standard statistical framework/protocol yet available in literature that can be used for EB data analysis towards discovery of biomarkers for use in a typical clinical setup. Nevertheless, EB analysis has immense potential towards development of biomarkers for the early disease diagnosis of diseases.

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          Most cited references 257

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          HMDB: a knowledgebase for the human metabolome

          The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.
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            Electronic nose: current status and future trends.

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              Diagnosing lung cancer in exhaled breath using gold nanoparticles.

              Conventional diagnostic methods for lung cancer are unsuitable for widespread screening because they are expensive and occasionally miss tumours. Gas chromatography/mass spectrometry studies have shown that several volatile organic compounds, which normally appear at levels of 1-20 ppb in healthy human breath, are elevated to levels between 10 and 100 ppb in lung cancer patients. Here we show that an array of sensors based on gold nanoparticles can rapidly distinguish the breath of lung cancer patients from the breath of healthy individuals in an atmosphere of high humidity. In combination with solid-phase microextraction, gas chromatography/mass spectrometry was used to identify 42 volatile organic compounds that represent lung cancer biomarkers. Four of these were used to train and optimize the sensors, demonstrating good agreement between patient and simulated breath samples. Our results show that sensors based on gold nanoparticles could form the basis of an inexpensive and non-invasive diagnostic tool for lung cancer.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Metabolites
                Metabolites
                metabolites
                Metabolites
                MDPI
                2218-1989
                09 January 2015
                March 2015
                : 5
                : 1
                : 3-55
                Affiliations
                [1 ]CQM—Centro de Química da Madeira, Universidade da Madeira, Campus Universitário da Penteada, Funchal 9000-390, Portugal; E-Mails: priscillaportofigueira@ 123456gmail.com (P.P.-F.); carina-cavaco@ 123456hotmail.com (C.C.); jsc@ 123456uma.pt (J.S.C.)
                [2 ]Proteomics Lab, National Centre for Cell Science, Ganeshkhind, Pune 411007, India; E-Mails: khushmanlord@ 123456gmail.com (K.T.); rsrikanth@ 123456nccs.res.in (S.R.)
                [3 ]Laboratory of Computational Biology, Centre for DNA Fingerprinting & Diagnostics, Hyderabad, Andhra Pradesh 500 001, India; E-Mails: rahuldhakne@ 123456cdfd.org.in (R.D.); han@ 123456cdfd.org.in (H.N.)
                [4 ]Centro de Ciências Exatas e da Engenharia da Universidade da Madeira, Campus Universitário da Penteada, Funchal 9000-390, Portugal
                Author notes
                [* ]Author to whom correspondence should be addressed; E-Mail: jamp@ 123456uma.pt ; Tel.: +351-291-705-131; Fax: +351-291-705-149.
                Article
                metabolites-05-00003
                10.3390/metabo5010003
                4381289
                25584743
                © 2015 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/4.0/).

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