AUTHOR=Li Kun , Fang Shiqiang , Wu Tangwei , Zheng Chao , Zeng Yi , He Jinrong , Zhang Yingmiao , Lu Zhongxin 

TITLE=Aptamer-functionalized graphene quantum dots combined with artificial intelligence detect bacteria for urinary tract infections

JOURNAL=Frontiers in Cellular and Infection Microbiology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1555617

DOI=10.3389/fcimb.2025.1555617

ISSN=2235-2988

ABSTRACT=ObjectivesUrinary tract infection is one of the most prevalent forms of bacterial infection, and prompt and efficient identification of pathogenic bacteria plays a pivotal role in the management of urinary tract infections. In this study, we propose a novel approach utilizing aptamer-functionalized graphene quantum dots integrated with an artificial intelligence detection system (AG-AI detection system) for rapid and highly sensitive detection of Escherichia coli (E. coli).MethodsFirstly, graphene quantum dots were modified with the aptamer that can specifically recognize and bind to E. coli. Therefore, the fluorescence intensity of graphene quantum dots was positively correlated with the concentration of E. coli. Finally, the fluorescence images were processed by artificial intelligence system to obtain the result of bacterial concentration.ResultsThe AG-AI detection system, with wide linearity (103-109 CFU/mL) and a low detection limit (3.38×102 CFU/mL), can effectively differentiate between E. coli and other urinary tract infection bacteria. And the result of detection system is in good agreement with MALDI-TOF MS.ConclusionsThe detection system is an accurate and effective way to detect bacteria in urinary tract infections.