Literature DB >> 30916881

Fast and reliable determination of Escherichia coli susceptibility to antibiotics: Infrared microscopy in tandem with machine learning algorithms.

Uraib Sharaha1, Eladio Rodriguez-Diaz2,3, Orli Sagi4, Klaris Riesenberg5, Ahmad Salman6, Irving J Bigio2,7, Mahmoud Huleihel1.   

Abstract

Antimicrobial drugs have an important role in controlling bacterial infectious diseases. However, the increasing resistance of bacteria to antibiotics has become a global health care problem. Rapid determination of antimicrobial susceptibility of clinical isolates is often crucial for the optimal antimicrobial therapy. The conventional methods used in medical centers for susceptibility testing are time-consuming (>2 days). Two bacterial culture steps are needed, the first is used to grow the bacteria from urine on agar plates to determine the species of the bacteria (~24 hours). The second culture is used to determine the susceptibility by growing colonies from the first culture for another 24 hours. Here, the main goal is to examine the potential of infrared microscopy combined with multivariate analysis, to reduce the time it takes to identify Escherichia coli susceptibility to antibiotics and to determine the optimum choice of antibiotic to which the bacteria will respond. E coli colonies of the first culture from patients with urinary tract infections (UTI) were examined for the bacterial susceptibility using Fourier-transform infrared (FTIR). Our results show that it is possible to determine the optimum choice of antibiotic with better than 89% sensitivity, in the time span of few minutes, following the first culture.
© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  SVM; bacterial resistance to antibiotics; infrared spectroscopy; multivariate analysis

Year:  2019        PMID: 30916881     DOI: 10.1002/jbio.201800478

Source DB:  PubMed          Journal:  J Biophotonics        ISSN: 1864-063X            Impact factor:   3.207


  4 in total

1.  Using an ATR-FTIR Technique to Detect Pathogens in Patients with Urinary Tract Infections: A Pilot Study.

Authors:  Sheng-Wei Pan; Hsiao-Chi Lu; Jen-Iu Lo; Li-Ing Ho; Ton-Rong Tseng; Mei-Lin Ho; Bing-Ming Cheng
Journal:  Sensors (Basel)       Date:  2022-05-10       Impact factor: 3.847

Review 2.  Advances in Optical Detection of Human-Associated Pathogenic Bacteria.

Authors:  Andrea Locke; Sean Fitzgerald; Anita Mahadevan-Jansen
Journal:  Molecules       Date:  2020-11-11       Impact factor: 4.411

3.  Prediction of itraconazole minimum inhibitory concentration for Fonsecaea pedrosoi using Fourier Transform Infrared Spectroscopy (FTIR) and chemometrics.

Authors:  Alessandra Koehler; Valeriano Antonio Corbellini; Daiane Heidrich; Maria Lúcia Scroferneker
Journal:  PLoS One       Date:  2020-12-02       Impact factor: 3.240

4.  Bioinformatics methods for identification of amyloidogenic peptides show robustness to misannotated training data.

Authors:  Michał Burdukiewicz; Malgorzata Kotulska; Natalia Szulc; Marlena Gąsior-Głogowska; Jakub W Wojciechowski; Jarosław Chilimoniuk; Paweł Mackiewicz; Tomas Šneideris; Vytautas Smirnovas
Journal:  Sci Rep       Date:  2021-04-26       Impact factor: 4.379

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.