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Miniaturised platforms for rapid diagnosis of antibiotic resistance

Miniaturised platforms for rapid diagnosis of antibiotic resistance
Miniaturised platforms for rapid diagnosis of antibiotic resistance
Pathogenic bacteria are biological cells that can cause infectious diseases. Antimicrobial resistance (AMR) is a phenomenon whereby pathogenic bacteria can survive even after exposure to previous effective antibiotics. If necessary action is not taken, AMR is predicted to be the leading cause of mortality. Antibiotic efficiency against bacteria is determined by antimicrobial susceptibility testing (AST). Conventional AST methods are time-consuming because they determine growth after many doubling times and each doubling time is at least 20 minutes. The lack of time efficient diagnostic tools accelerates AMR because antibiotics are usually prescribed without the susceptibility test results. This research describes the development of three miniaturised AST systems that are fast-test, low-cost and high-sensitivity. The first detection system is a dielectrophoresis (DEP) enhanced optical system that can detect the presence of β-lactamases from a minimum of 103 colony-forming unit (CFU)/mL bacterial sample in 1 hour. The bacteria in a test sample were first enriched by filtering followed by DEP concentration. Then, the presence of β-lactamases was determined through a colour change using the dye nitrocefin, a β-lactam analogue, in an optical chip. The sensitivity of the DEPenhanced optical system is four orders of magnitude better than conventional plate-based assays and the clinical selectivity is 100%. The second detection system is a miniature pH system that can detect the presence of βlactamases from a low concentration sample of 105 CFU/mL bacterial sample in 1 hour. An iridium oxide pH sensor is used to detect the pH reduction due to β-lactam antibiotic hydrolysis. This pH system is robust, simple and suitable for application in point-of-care situations. The sensitivity of the pH system is two orders of magnitude better than conventional plate-based assays and the clinical selectivity is 75%. The third detection system measures the impedance of a suspension of bacteria exposed to antibiotics to infer growth rate and susceptibility, which can indicate the minimum inhibitory concentration (MIC) of β-lactam antibiotics, ciprofloxacin, gentamicin, ceftazidime, colistin and doxycycline for various species of bacteria. The MIC detected by the impedance system can be done in 1 hour and shows >90% concordance with conventional broth dilution method that takes over 20 hours. In summary, this thesis describes three miniaturized, AST systems aimed at rapid diagnosis of the presence of β-lactamases or broadband antibiotic susceptibility within 1 hour with low cost and high sensitivity.
University of Southampton
Li, Yuetao
32f69867-9a4d-4a10-b5a6-719cda207746
Li, Yuetao
32f69867-9a4d-4a10-b5a6-719cda207746
Morgan, Hywel
de00d59f-a5a2-48c4-a99a-1d5dd7854174

Li, Yuetao (2020) Miniaturised platforms for rapid diagnosis of antibiotic resistance. Doctoral Thesis, 223pp.

Record type: Thesis (Doctoral)

Abstract

Pathogenic bacteria are biological cells that can cause infectious diseases. Antimicrobial resistance (AMR) is a phenomenon whereby pathogenic bacteria can survive even after exposure to previous effective antibiotics. If necessary action is not taken, AMR is predicted to be the leading cause of mortality. Antibiotic efficiency against bacteria is determined by antimicrobial susceptibility testing (AST). Conventional AST methods are time-consuming because they determine growth after many doubling times and each doubling time is at least 20 minutes. The lack of time efficient diagnostic tools accelerates AMR because antibiotics are usually prescribed without the susceptibility test results. This research describes the development of three miniaturised AST systems that are fast-test, low-cost and high-sensitivity. The first detection system is a dielectrophoresis (DEP) enhanced optical system that can detect the presence of β-lactamases from a minimum of 103 colony-forming unit (CFU)/mL bacterial sample in 1 hour. The bacteria in a test sample were first enriched by filtering followed by DEP concentration. Then, the presence of β-lactamases was determined through a colour change using the dye nitrocefin, a β-lactam analogue, in an optical chip. The sensitivity of the DEPenhanced optical system is four orders of magnitude better than conventional plate-based assays and the clinical selectivity is 100%. The second detection system is a miniature pH system that can detect the presence of βlactamases from a low concentration sample of 105 CFU/mL bacterial sample in 1 hour. An iridium oxide pH sensor is used to detect the pH reduction due to β-lactam antibiotic hydrolysis. This pH system is robust, simple and suitable for application in point-of-care situations. The sensitivity of the pH system is two orders of magnitude better than conventional plate-based assays and the clinical selectivity is 75%. The third detection system measures the impedance of a suspension of bacteria exposed to antibiotics to infer growth rate and susceptibility, which can indicate the minimum inhibitory concentration (MIC) of β-lactam antibiotics, ciprofloxacin, gentamicin, ceftazidime, colistin and doxycycline for various species of bacteria. The MIC detected by the impedance system can be done in 1 hour and shows >90% concordance with conventional broth dilution method that takes over 20 hours. In summary, this thesis describes three miniaturized, AST systems aimed at rapid diagnosis of the presence of β-lactamases or broadband antibiotic susceptibility within 1 hour with low cost and high sensitivity.

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Published date: August 2020

Identifiers

Local EPrints ID: 448004
URI: http://eprints.soton.ac.uk/id/eprint/448004
PURE UUID: e56e3fd2-8ce4-4a0a-a769-ef6d1e7d2ead
ORCID for Hywel Morgan: ORCID iD orcid.org/0000-0003-4850-5676

Catalogue record

Date deposited: 30 Mar 2021 16:31
Last modified: 13 Apr 2021 01:40

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Contributors

Author: Yuetao Li
Thesis advisor: Hywel Morgan ORCID iD

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