1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Fluorescent Sensor Arrays Can Predict and Quantify the Composition of Multicomponent Bacterial Samples

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Fast and reliable identification of infectious disease agents is among the most important challenges for the healthcare system. The discrimination of individual components of mixed infections represents a particularly difficult task. In the current study we further expand the functionality of a ratiometric sensor array technology based on small-molecule environmentally-sensitive organic dyes, which can be successfully applied for the analysis of mixed bacterial samples. Using pattern recognition methods and data from pure bacterial species, we demonstrate that this approach can be used to quantify the composition of mixtures, as well as to predict their components with the accuracy of ~80% without the need to acquire additional reference data. The described approach significantly expands the functionality of sensor arrays and provides important insights into data processing for the analysis of other complex samples.

          Related collections

          Most cited references24

          • Record: found
          • Abstract: found
          • Article: not found

          Methods for the detection and identification of pathogenic bacteria: past, present, and future.

          In order to retard the rate of development of antibacterial resistance, the causative agent must be identified as rapidly as possible, so that directed patient treatment and/or contact precautions can be initiated. This review highlights the challenges associated with the detection and identification of pathogenic bacteria, by providing an introduction to the techniques currently used, as well as newer techniques that are in development. Focusing on the chemical basis for these techniques, the review also provides a comparison of their advantages and disadvantages.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Rapid and efficient identification of bacteria using gold-nanoparticle-poly(para-phenyleneethynylene) constructs.

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Using discriminant analysis for multi-class classification: an experimental investigation

                Bookmark

                Author and article information

                Contributors
                Journal
                Front Chem
                Front Chem
                Front. Chem.
                Frontiers in Chemistry
                Frontiers Media S.A.
                2296-2646
                15 January 2020
                2019
                : 7
                : 916
                Affiliations
                [1] 1Department of Pharmaceutical Sciences, University of Nebraska Medical Center , Omaha, NE, United States
                [2] 2Department of Pathology and Microbiology, University of Nebraska Medical Center , Omaha, NE, United States
                [3] 3Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center , Omaha, NE, United States
                [4] 4Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center , Omaha, NE, United States
                Author notes

                Edited by: Jinsong Han, China Pharmaceutical University, China

                Reviewed by: Benhua Wang, Central South University, China; Xiaolong Sun, Xi'an Jiaotong University (XJTU), China

                *Correspondence: Denis Svechkarev denis.svechkarev@ 123456unmc.edu

                This article was submitted to Analytical Chemistry, a section of the journal Frontiers in Chemistry

                Article
                10.3389/fchem.2019.00916
                6974461
                ec91b4ea-5920-4601-9fe5-592e8fbc87c2
                Copyright © 2020 Svechkarev, Sadykov, Houser, Bayles and Mohs.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 November 2019
                : 17 December 2019
                Page count
                Figures: 4, Tables: 2, Equations: 1, References: 26, Pages: 7, Words: 4470
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: 1S10RR027940
                Award ID: 1S10RR17846
                Award ID: P01 AI83211
                Award ID: P20 GM103480
                Award ID: P30 CA036727
                Award ID: R01 AI125589
                Award ID: R01 EB027662
                Categories
                Chemistry
                Original Research

                multiparametric sensing,3-hydroxyflavone,esipt,pathogenic bacteria,discriminant analysis,machine learning,pattern analysis

                Comments

                Comment on this article