Moreover, by further analyzing patients’ proteomic data with artificial intelligence (AI), the researchers developed a highly accurate four-protein diagnostic signature, including Lymphocyte Cytosolic Protein (LCP1), Fc Gamma Receptor IIIa (FCGR3A also known as CD16a), Alpha-1-antichymotrypsin (SERPINA3) and Butyrylcholinesterase (BCHE), that could discriminate children with MIS-C from children with bacterial and viral infections [Area Under the Curve (AUC): 100%] [11]. The gene discussed is FCGR3A; the disease is viral infectious disease.