JOURNAL ARTICLE

Evaluation of a new rapid immunochromatographic assay for the detection of GES-producing Gram-negative bacteria.

  • Published In: Journal of Antimicrobial Chemotherapy (JAC), 2023, v. 78, n. 5. P. 1282 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Gonzalez, Camille; Volland, Hervé; Oueslati, Saoussen; Niol, Léa; Legrand, Camille; Francius, Laura; Chalin, Arnaud; Vogel, Anaïs; Simon, Stéphanie; Naas, Thierry 3 of 3

Abstract

The article focuses on the development and evaluation of a lateral flow immunoassay (LFIA) prototype for the rapid detection of GES-type β-lactamases, a group of minor carbapenemases increasingly identified in Gram-negative bacteria such as Enterobacterales, Pseudomonas aeruginosa, and Acinetobacter baumannii. The GES LFIA demonstrated 100% sensitivity and specificity in detecting prevalent GES variants from bacterial cultures grown on various media, although it cannot differentiate between GES extended-spectrum β-lactamases (ESBLs) and carbapenemases. Given that most commercial assays target only the five main carbapenemases (KPC, NDM, VIM, IMP, and OXA-48), combining the GES LFIA with existing tests could enhance detection coverage, particularly in clinical settings where GES enzymes are emerging. The assay is rapid (results within 15 minutes), user-friendly, and suitable for routine clinical microbiology workflows, but further validation on additional GES variants and clinical samples is needed.

Additional Information

  • Source:Journal of Antimicrobial Chemotherapy (JAC). 2023/05, Vol. 78, Issue 5, p1282
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2023
  • ISSN:0305-7453
  • DOI:10.1093/jac/dkad090
  • Accession Number:163492372
  • Copyright Statement:Copyright of Journal of Antimicrobial Chemotherapy (JAC) is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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