Multilevel Hydrangea-like Heterogeneous Oxides Enabling COVID-19 Progression Surveillance via Metabolic Fingerprints

Coronavirus disease 2019 (COVID-19), a global pandemic infectious disease, requires early diagnosis and dynamic monitoring to enable timely intervention and reduce the risks of adverse outcomes. To support these needs, we developed an advanced metabolic monitoring platform by incorporating a silica template to engineer a hierarchical interlocked and closely intertwined multilevel hydrangea-like heterogeneous oxide (dubbed as MHHOs) matrix to extract serum metabolic fingerprints (SMFs) efficiently for early diagnosis and monitoring COVID-19 progression. Decoded by machine learning algorithms, this approach achieves 100% classification accuracy in distinguishing between different stages of the COVID-19 progression. Furthermore, the integrated screening model, optimized in accordance with clinical requirements, consistently surpasses a threshold of 0.98 across multiple key performance metrics, including accuracy, precision, recall, F1 score, and Matthews correlation coefficient, thereby demonstrating its robustness and practical applicability in clinical settings. In addition, by investigating the associated pathways and expression patterns of key biomarkers throughout disease progression, we explored their dynamic regulatory mechanism, enhancing the characterization of COVID-19 pathological features. Collectively, this work provides a perspective for the metabolite-based analytical platform and holds promise for advancing the clinical implementation of personalized therapeutic strategies.