Identification of Antibiotic Resistance in ESKAPE Pathogens through Plasmonic Nanosensors and Machine Learning

Antibiotic-resistant ESKAPE pathogens cause nosocomial infections that lead to huge morbidity and mortality worldwide. Rapid identification of antibiotic resistance is vital for the prevention and control of nosocomial infections. However, current techniques like genotype identification and antibiotic susceptibility testing are generally time-consuming and require large-scale equipment. Herein, we develop a rapid, facile, and sensitive technique to determine the antibiotic resistance phenotype among ESKAPE pathogens through plasmonic nanosensors and machine learning. Key to this technique is the plasmonic sensor array that contains gold nanoparticles functionalized with peptides differing in hydro-phobicity and surface charge. The plasmonic nanosensors can interact with pathogens to generate bacterial fingerprints that alter the surface plasmon resonance (SPR) spectra of nanoparticles. In combination with machine learning, it enables the identification of antibiotic resistance among 12 ESKAPE pathogens in less than 20 min with an overall accuracy of 89.74%. This machine-learning-based approach allows for the identification of antibiotic-resistant pathogens from patients and holds great promise as a clinical tool for biomedical diagnosis.

Automated summary

We developed a rapid, facile, and sensitive technique for determining the antibiotic resistance phenotype among ESKAPE pathogens using plasmonic nanosensors and machine learning. This machine-learning-based approach can identify antibiotic-resistant pathogens from patients within 20 minutes with an overall accuracy of 89.74%. It holds great promise as a clinical tool for biomedical diagnosis.