Wang Kaidong, Wang Shaolei, Margolis Samuel, Cho Jae Min, Zhu Enbo, Dupuy Alexander, Yin Junyi, Park Seul-Ki, Magyar Clara E, Adeyiga Oladunni B, Jensen Kristin Schwab, Belperio John A, Passam Freda, Zhao Peng, Hsiai Tzung K
Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.
Department of Bioengineering, Henry Samueli School of Engineering and Applied Science, University of California Los Angeles, Los Angeles, CA 90095, USA.
Sci Adv. 2024 Dec 13;10(50):eadq6778. doi: 10.1126/sciadv.adq6778. Epub 2024 Dec 11.
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 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.
近期的新型冠状病毒肺炎大流行凸显了对临床血栓形成事件进行快速准确预测的必要性。在此，我们开发了纳米工程多通道免疫传感器，用于快速检测与血栓形成相关的循环生物标志物，包括C反应蛋白（CRP）、钙卫蛋白、可溶性血小板选择素（sP-选择素）和D-二聚体。我们使用碳纳米管的光纤激光雕刻和微流控通道的CO激光切割来制造免疫传感器，并通过金纳米颗粒的电化学沉积使其与生物标志物特异性适配体和抗体结合。基于四种生物标志物浓度进行无监督聚类，我们在53例患者中的49例中预测了血栓形成事件。与单个生物标志物CRP（0.773）、钙卫蛋白（0.711）、sP-选择素（0.683）和D-二聚体（0.739）的受试者工作特征曲线下面积（AUC）值相比，四种生物标志物组合的AUC为0.95，表明对急性血栓形成预测具有高灵敏度和特异性。因此，具有无监督聚类的纳米工程多通道平台为预测血栓形成、指导个性化医疗提供了准确有效的方法。
