Rapid prediction of acute thrombosis via nanoengineered immunosensors with unsupervised clustering for multiple circulating biomarkers | Science Advances

The recent SARS-CoV-2 pandemic underscores the need for rapid and accurate prediction of clinical thrombotic events. Here, we developed nanoengineered multichannel immunosensors for rapid detection of circulating biomarkers associated with thrombosis, including C-reactive protein (CRP), calprotectin, soluble platelet selectin (sP-selectin), and D-dimer. We fabricated the immunosensors using fiber laser engraving of carbon nanotubes and CO ₂ laser cutting of microfluidic channels, along with the electrochemical deposition of gold nanoparticles to conjugate with biomarker-specific aptamers and antibody. Using unsupervised clustering based on four biomarker concentrations, we predicted thrombotic events in 49 of 53 patients. The four-biomarker combination yielded an area under the receiver operating characteristic curve (AUC) of 0.95, demonstrating high sensitivity and specificity for acute thrombosis prediction compared to the AUC values for individual biomarkers: CRP (0.773), calprotectin (0.711), sP-selectin (0.683), and D-dimer (0.739). Thus, a nanoengineered multichannel platform with unsupervised clustering provides accurate and efficient methods for predicting thrombosis, guiding personalized medicine.