Discrimination of Respiratory Tract Infections by a Reduced Graphene Oxide Array Modified with Metal−Organic Frameworks and Metal Phthalocyanines

As a prevalent clinical condition, it is critical to distinguish between bacterial and viral respiratory tract infections given their pivotal role in guiding appropriate pharmaceutical interventions and preventing antibiotic misuse. Exhaled breath (EB) contains a spectrum of disease-specific biomarkers, enabling precise diagnostic analysis. Thus, EB analysis using an electronic nose (e-nose) to record electrical response fingerprints and discriminate pathogens via machine learning algorithms has emerged as a promising noninvasive diagnostic technology. In this study, a graphene-based e-nose sensor array modified with metal-organic frameworks (MOFs) and metal phthalocyanines (MPcs) was developed by using multiple reduction methods. The sensor array demonstrated excellent capability in distinguishing between two types of EB samples collected from healthy individuals spiked with acetone and isoprene, which are closely associated with bacterial and viral respiratory infections. Furthermore, a diagnostic model was constructed using e-nose fingerprints from 145 clinical EB samples comprising 89 bacterial infection cases and 56 viral infection cases. A weighted fusion classification model, integrating the support vector machine, random forest, and Lasso regression (Lasso), achieved an accuracy of 83.7% in the validation group, with an area under the curve (AUC) of 0.87. An independent external clinical trial involving 43 respiratory infection patients (including 6 unidentified cases) yielded an accuracy of 75.7% and an AUC of 0.81 for distinguishing bacterial from viral infections. Additionally, the sensor array achieved a 75% accuracy rate in discriminating mycoplasma infections by using linear discriminant analysis. These results suggest that the graphene-based e-nose array modified with MOFs and MPcs is a promising tool for diagnosing respiratory tract infections, aiding in optimized treatment decisions and potentially improving therapeutic efficiency.