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Related Experiment Video

Updated: Jun 23, 2026

One-day Workflow Scheme for Bacterial Pathogen Detection and Antimicrobial Resistance Testing from Blood Cultures

One-day Workflow Scheme for Bacterial Pathogen Detection and Antimicrobial Resistance Testing from Blood Cultures

Published on: July 9, 2012

Simpler Procedure and Improved Performance for Pathogenic Bacteria Analysis with a Paper-Based Ratiometric

Aayushi Laliwala¹, Denis Svechkarev¹, Marat R Sadykov²

¹Department of Pharmaceutical Sciences, University of Nebraska Medical Center, Omaha, Nebraska 68198-6858, United States.

Analytical Chemistry

|January 24, 2022

Summary

This summary is machine-generated.

A novel paper-based fluorescent sensor array rapidly identifies bacterial species and Gram status with over 90% accuracy. This low-cost diagnostic tool offers a promising solution for clinical applications, especially in resource-limited settings.

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Colorimetric Paper-based Detection of Escherichia coli, Salmonella spp., and Listeria monocytogenes from Large Volumes of Agricultural Water

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Published on: June 9, 2014

Area of Science:

Biomedical Engineering
Analytical Chemistry
Microbiology

Background:

Bacterial infections cause significant global morbidity and mortality, often due to delayed or inaccurate identification.
Conventional diagnostic methods are time-consuming, costly, and require specialized resources.
Rapid and accurate pathogen identification is crucial for effective treatment.

Purpose of the Study:

To develop a rapid, accurate, and cost-effective paper-based sensor for bacterial identification.
To create a diagnostic tool suitable for low-resource environments.
To improve upon the limitations of traditional bacterial diagnostic techniques.

Main Methods:

Fabrication of a paper-based ratiometric fluorescent sensor array using photolithography and environment-sensitive dyes (3-hydroxyflavone derivatives).
Interaction of the sensor array with bacterial cell envelopes to generate unique fluorescence response patterns.
Analysis of fluorescence patterns using neural networks for bacterial species and Gram status identification.

Main Results:

The sensor array achieved over 90% accuracy in identifying 16 bacterial species and their Gram status.
The paper-based sensor demonstrated stability for up to 6 months.
The developed sensor required 30 times less dye and sample volume compared to solution-based methods.

Conclusions:

The paper-based fluorescent sensor array provides a rapid and accurate method for bacterial identification.
This technology has the potential for clinical translation, particularly in resource-limited settings.
The sensor offers a cost-effective and efficient alternative to conventional diagnostic techniques.