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Machine Learning-Assistant Colorimetric Sensor Arrays for Intelligent and Rapid Diagnosis of Urinary Tract Infection.

Accepted version
Peer-reviewed

Type

Article

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Authors

Li, Ge 
Su, Xiaozhi 
Xu, Dong 

Abstract

Urinary tract infections (UTIs), which can lead to pyelonephritis, urosepsis, and even death, are among the most prevalent infectious diseases worldwide, with a notable increase in treatment costs due to the emergence of drug-resistant pathogens. Current diagnostic strategies for UTIs, such as urine culture and flow cytometry, require time-consuming protocols and expensive equipment. We present here a machine learning-assisted colorimetric sensor array based on recognition of ligand-functionalized Fe single-atom nanozymes (SANs) for the identification of microorganisms at the order, genus, and species levels. Colorimetric sensor arrays are built from the SAN Fe1-NC functionalized with four types of recognition ligands, generating unique microbial identification fingerprints. By integrating the colorimetric sensor arrays with a trained computational classification model, the platform can identify more than 10 microorganisms in UTI urine samples within 1 h. Diagnostic accuracy of up to 97% was achieved in 60 UTI clinical samples, holding great potential for translation into clinical practice applications.

Description

Keywords

Biosensing Techniques, Colorimetry, Humans, Iron, Machine Learning, Urinary Tract Infections, colorimetric sensor array, Fe single-atom nanozyme, machine learning, microorganism identification, urinary tract infections

Journal Title

ACS Sensors

Conference Name

Journal ISSN

2379-3694
2379-3694

Volume Title

9

Publisher

American Chemical Society