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Related Experiment Video

Updated: May 22, 2026

Visualization of Bacterial Resistance using Fluorescent Antibiotic Probes
08:23

Visualization of Bacterial Resistance using Fluorescent Antibiotic Probes

Published on: March 2, 2020

Artificial intelligence-assisted phenotyping of drug-resistant bacteria using a monosaccharide-based fluorescent

Zhuo-Fan Zhang1, Wen-Zhen Gui1, Yi-Fan Tang2

  • 1Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Centre, School of Chemistry and Molecular Engineering. East China University of Science & Technology Shanghai 200237 China xlhu@ecust.edu.cn xphe@ecust.edu.cn.

Chemical Science
|May 21, 2026
PubMed
Summary

This study introduces a novel sensor array using fluorescently labeled sugars and artificial intelligence (AI) to rapidly identify drug-resistant bacteria. This approach aids in developing targeted antibiotic therapies for improved patient outcomes.

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Last Updated: May 22, 2026

Visualization of Bacterial Resistance using Fluorescent Antibiotic Probes
08:23

Visualization of Bacterial Resistance using Fluorescent Antibiotic Probes

Published on: March 2, 2020

Area of Science:

  • Biochemistry
  • Microbiology
  • Analytical Chemistry

Background:

  • Conventional methods for determining bacterial antibiotic susceptibility are time-consuming and labor-intensive.
  • Effective phenotyping of drug-resistant bacteria is crucial for improving therapeutic strategies against bacterial infections.
  • Development of rapid and accurate diagnostic tools is needed to combat antimicrobial resistance.

Purpose of the Study:

  • To develop a novel sensor array for accurate phenotyping of drug-resistant bacteria using artificial intelligence (AI).
  • To create a high-throughput screening method for bacterial susceptibility testing.
  • To enable precision medicine approaches for treating bacterial infections.

Main Methods:

  • Development of a sensor array comprising eight fluorescent glycoprobes by labeling monosaccharides (d-glucose, d-galactose, l-fucose, d-mannose) with a fluorophore (DPAC).
  • Homogeneous high-throughput screening of bacterial strains using the glycoprobe array.
  • Application of ensemble learning (AI) for signal processing and discrimination of bacterial strains.
  • Phenotyping of *Pseudomonas aeruginosa* after antibiotic exposure.

Main Results:

  • The glycoprobe sensor array showed sensitive ratiometric fluorescence changes with *Pseudomonas aeruginosa* strains expressing specific lectins (LecA, LecB).
  • Minimal fluorescence changes were observed with bacterial strains lacking these lectins, indicating specificity.
  • AI-assisted analysis accurately discriminated between drug-resistant and drug-sensitive *P. aeruginosa* isolates.
  • The system successfully phenotyped *P. aeruginosa* following long-term antibiotic exposure.

Conclusions:

  • The developed fluorescent glycoprobe sensor array coupled with AI offers a rapid and accurate method for bacterial drug resistance phenotyping.
  • This approach holds significant potential for advancing precision medicine by enabling timely and targeted therapeutic interventions.
  • The technology provides a powerful tool for combating antimicrobial resistance and improving treatment efficacy.