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This study introduces a new chemical-nose sensing method for rapid pathogen identification by analyzing metabolic variations. This approach accurately distinguishes bacteria, offering a robust tool for pathogen surveillance.

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Area of Science:

  • Biotechnology
  • Analytical Chemistry
  • Microbiology

Background:

  • Chemical-nose strategies show promise for pathogen identification but are limited by environmental interference due to non-specific interactions.
  • Existing methods struggle with environmental variability, impacting practical applications in pathogen detection.

Purpose of the Study:

  • To develop a novel chemical-nose sensing approach for rapid and accurate pathogen discrimination.
  • To leverage dynamic metabolic variations in peptidoglycan metabolism for enhanced specificity.
  • To overcome the limitations of traditional methods by improving anti-interference capabilities.

Main Methods:

  • Metabolic labeling of pathogens with clickable handles at different pH levels (5 and 7) and time points (20 and 60 min).
  • Click reaction with fluorescence up-conversion nanoparticles to generate a four-dimensional signal output.
  • Classification of eight model bacteria strains into strains, genera, and Gram phenotypes using the multi-dimensional signal.

Main Results:

  • Successful classification of eight model bacteria into strains, genera, and Gram phenotypes.
  • Demonstrated high anti-interference capability by correlating signal differences with enzyme activity in metabolic labeling.
  • Achieved 100% accuracy in identifying pathogens in spiked urinary samples within 2 hours.
  • Enabled classification of unknown species into correct phenotypes, validating the approach's robustness.

Conclusions:

  • The novel chemical-nose sensing approach offers precise and rapid pathogen identification with excellent anti-interference capabilities.
  • This method, based on dynamic metabolic variations, shows significant promise for widespread application in pathogen identification and surveillance.
  • The four-dimensional signal output provides a robust platform for distinguishing bacterial species and phenotypes.