Work

Species-specific discrimination of bacterial biofilms using a ratiometric fluorescence sensor array and machine learning

Year: 2025

Type: article

Abstract: as low as 0.002 o.u. Additionally, using neural networks, the sensor array achieves more than 95% accuracy in distinguishing planktonic bacteria from biofilms and shows over 85% accuracy in identifying clinical bacterial species and biofilms. These findings highlight the sensor's potential for high-precision biofilm identification in laboratory and clinical settings, offering a valuable tool for advancing biofilm research and enhancing clinical diagnostics.

Source: Sensors & Diagnostics

DOI: 10.1039/d5sd00152h

Authors: Ritika Gupta, Aayushi Laliwala, Elena Muldiiarova, Kenneth W. Bayles, Denis Svechkarev, Marat R. Sadykov, Aaron M. Mohs

Institutions: University of Nebraska Medical Center, University of Nebraska at Omaha, Fred and Pamela Buffett Cancer Center

Language: English

Topic: Bacterial biofilms and quorum sensing

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: diamond

Funders: National Institutes of Health, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Center for Research Resources

Awards: Staphylococcal Biofilm and Disease, Nebraska Center for Nanomedicine, Upgrade of mass spectrometer facility, Replacement of GC Mass Spectrometer, G9000008107, G9000108264, G9000158362, G9000164627, G9000297044, Buffett Cancer Center Support Grant

OpenAlex ID: W4416910290

FWCI: 0.505

Cites: 31

Cited by: 2

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