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Bacterial Detection & Identification Using Electrochemical Sensors
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Machine learning assisted identification of antibiotic-resistant Staphylococcus aureus strains using a paper-based

Aayushi Laliwala1, Ritika Gupta1, Denis Svechkarev2

  • 1Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, Nebraska 68198-6858, USA.

Microchemical Journal : Devoted to the Application of Microtechniques in All Branches of Science
|July 30, 2025
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Summary

A novel paper-based sensor array can rapidly identify antibiotic-resistant Staphylococcus aureus strains, including MRSA and VISA, and their biofilms. This technology offers a promising, accurate, and accessible diagnostic tool for clinical settings.

Keywords:
Antibiotic-resistanceBiofilmsDifferential sensingMultivariate analysisPattern analysisS. aureus

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Area of Science:

  • Biomedical Engineering
  • Microbiology
  • Analytical Chemistry

Background:

  • Staphylococcus aureus is a significant human pathogen, causing diverse infections and posing challenges due to antibiotic resistance and biofilm formation.
  • Current methods for identifying antimicrobial-resistant (AMR) S. aureus strains like MRSA and VISA are often time-consuming and expensive.
  • Previous work developed a paper-based sensor array with fluorescent dyes and machine learning for bacterial species and Gram status identification.

Purpose of the Study:

  • To evaluate a paper-based ratiometric sensor array's ability to distinguish antibiotic-resistant Staphylococcus aureus strains and their biofilms.
  • To assess the sensor array's performance in conjunction with machine learning algorithms for accurate and rapid identification of AMR S. aureus.

Main Methods:

  • Utilized a paper-based sensor array with fluorescent sensor dyes (3-hydroxyflavone derivatives) pre-adsorbed on paper microzone plates.
  • Employed machine learning algorithms including Linear Discriminant Analysis (LDA), neural networks, and support vector machines (SVM).
  • Tested the sensor array on methicillin-resistant S. aureus (MRSA), methicillin-susceptible S. aureus (MSSA), and vancomycin-intermediate S. aureus (VISA) strains, including their biofilms.

Main Results:

  • The sensor array differentiated MRSA from MSSA strains with 82.5% accuracy using LDA and neural networks.
  • Support vector machines achieved 97.5% accuracy in classifying MRSA, MSSA, and VISA clinical isolates.
  • The sensor array successfully discriminated AMR S. aureus biofilms with over 80% accuracy.

Conclusions:

  • The paper-based sensor array demonstrates significant potential as a robust diagnostic tool for identifying drug-resistant S. aureus.
  • The technology offers accurate, rapid, and easy identification of AMR S. aureus strains and biofilms in clinical settings.
  • This approach could overcome limitations of current time-consuming and expensive diagnostic methodologies.