"Nano-filter"-integrated AIMS with machine learning: direct exhaled breath analysis for lung cancer screening.

Exhaled breath (EB) harbors rich molecular information, providing important insights into multiple metabolism processes of the living body. Thus, EB analysis is believed to be a promising diagnostic method for fast and non-invasive disease detection in the future. In this work, we developed a cost-effective "nano-filter" integrated with ambient ionization mass spectrometry (AIMS) for the direct detection of EB aldehyde metabolites. The "nano-filter" features p-selenophenylhydrazide-functionalized silver nanoparticles (HSe-Ag NPs) immobilized on fiber paper, selectively capturing EB aldehydes while filtering interferents. Upon application of high voltage to induce cleavage of Ag-Se bonds, the Se-tagged aldehyde derivatives (Se-aldehydes) are liberated for AIMS detection. We demonstrated the high performance of this "nano-filter" AIMS strategy by analysing 152 clinical EB samples, including 91 healthy individuals and 61 lung cancer (LCa, non-small cell lung cancer) patients. Over 88 aldehydes were detected, most reported for the first time. Based on a machine learning (ML) model, the strategy achieved 95.8% accuracy in identifying LCa using these EB aldehydes. We believe that this novel nano-filter AIMS strategy, combined with the ML technique, can provide a robust and effective tool for high-throughput LCa screening for clinical diagnosis and biomedical research.