Today, early disease detection (EDD) is a matter of more importance than ever in medicine. Upon interaction with human plasma, nanoparticles are covered by proteins leading to formation of a biomolecular corona (BC). As the protein patterns of patients with conditions differ from those of healthy subjects, current research into technologies based on the exploitation of personalized BC patterns could be a turning point for early disease detection. Here, we present a framework based on principal component analysis of large personalized BC datasets. We comment on how principal component analysis of personalized BC data is a fundamental step towards turning the output of basic research into fast, safe and inexpensive technologies with superior prediction ability than current methods.

如今，早期疾病检测（EDD）在医学领域比以往任何时候都更加重要。与人血浆相互作用后，纳米颗粒被蛋白质覆盖，导致形成生物分子电晕（BC）。由于有条件的患者的蛋白质模式与健康受试者的蛋白质模式不同，因此基于个性化BC模式开发的当前技术研究可能会成为早期疾病检测的转折点。在这里，我们提出了一个基于大型个性化BC数据集主成分分析的框架。我们评论个性化BC数据的主成分分析是如何将基础研究的成果转变为具有比当前方法更好的预测能力的快速，安全和廉价的技术的基本步骤。