The machine learning classification accuracy analysis revealed a systematic and unique tailored architecture of RSN disruption. The classification accuracy ranking showed that the most affected networks for bvFTD were the SN + EN network pair (mean accuracy = 86.43%, AUC = 0.91, sensitivity = 86.45%, specificity = 87.54%); for AD, the DMN + EN…[Read more]

Our study underlines the importance of the morphological design at the interface and considers PDA as a promising bioinspired material to tailor interfacial interactions.N1,N1-diallyl-N6,N6,N6-tripropylhexane-1,6-diaminium chloride (NDTHDC) and its polymer poly(N1,N1-diallyl-N6,N6,N6-tripropylhexane-1,6-diaminium chloride) (poly-NDTHDC) were…[Read more]

 1C12, 1E12, and 2E1 displayed higher affinity for PF4/H complexes than 5B9 and KKO, comparable to human group-3 Abs. Only 1C12, 1E12, 2E1, and group-3 Abs formed large complexes with native PF4, and activated platelets without heparin. The predicted binding sites of 1C12, 1E12, and 2E1 on PF4 differed from those of KKO and 5B9, but were close t…[Read more]