PROBING THE MECHANISMS OF PLATELET ADHESION TO ADSORBED PLASMA PROTEINS

Despite over three decades of research in blood-material interactions, the biomaterials field has been unsuccessful in developing a truly non-thrombogenic biomaterial. This is due to an incomplete understanding of the factors underlying biomaterial-associated thrombosis, especially the mechanisms mediating the interactions of platelets with the adsorbed plasma protein layer(s) on the implant surface. The work presented here is motivated by the primary goal of delineating these mechanisms, and understanding the platelet receptors involved, as well as the domains/amino acid sequences they bind to in the protein molecules. It is critical to differentiate between the amount and the conformation of the adsorbed protein, both of which are potential mediators of platelet adhesion, while designing hemocompatible biomaterials. We accomplished this by independently varying the surface chemistry and protein solution concentration, and illustrated that the platelet adhesion correlated strongly with the degree of adsorption-induced fibrinogen (Fg) unfolding, as measure by the loss in alpha-helix measured via circular dichroism (CD) spectropolarimetry. Additionally, platelet adhesion to adsorbed albumin (Alb), which is conventionally thought to be unable to bind platelets, strongly correlated with Alb unfolding beyond a critical level of unfolding (~34% alpha-helix loss). A variety of blocking strategies were employed in order to identify the platelet receptors involved in the adhesion process, including soluble peptides, monoclonal antibodies, as well as a platelet antagonist drug. Our preliminary results suggested that two platelet receptor sets were potentially mediating the adhesion, as a peptide containing the Arginine-Glycine-Aspartic Acid (RGD) sequence, which is a well known cell-binding sequence, was found to be a partial inhibitor of platelet adhesion to both adsorbed Fg and Alb. We therefore hypothesized that one set was specific to the RGD amino acid sequence, and mediated both platelet adhesion and activation. The other set was likely non-RGD-specific and mediated adhesion with little/no activation. Targeting the GPIb-IX-V complex as the non-RGD-specific receptor set using monoclonal antibodies against GPIb, did not inhibit platelet adhesion to adsorbed Fg and Alb. However, the use of Aggrastat, a platelet antagonist drug against the RGD-specific GPIIb/IIIa platelet receptor, led to a near complete inhibition of platelet adhesion to both adsorbed Fg and Alb, clearly illustrating the critical role played by this receptor in platelet adhesion. Chemical modification of the arginine residues in adsorbed Alb led to a significant decrease in platelet adhesion to Alb, thereby provided deeper insight into their role in mediating platelet-Alb interactions, while the modification of lysine residues did not affect platelet adhesion. Thus, we hypothesize that beyond a critical degree of adsorption-induced conformational changes in Alb, the arginine and aspartic/glutamic acid residues may become spatially oriented such that they form RGD-like motifs which are recognized by the GPIIb/IIIa platelet receptors. Additionally, we also showed that an irreversibly adsorbed, tightly packed Alb layer undergoes increased unfolding with increasing residence times of up to 6 months, thereby enabling and enhancing platelet adhesion. Overall, these studies present deeper insights into the molecular mechanisms mediating platelet interactions with adsorbed plasma proteins, and how these interactions can be controlled to improve the hemocompatibility of cardiovascular biomaterials

Statistical guarantees for the EM algorithm: From population to sample-based analysis

We develop a general framework for proving rigorous guarantees on the performance of the EM algorithm and a variant known as gradient EM. Our analysis is divided into two parts: a treatment of these algorithms at the population level (in the limit of infinite data), followed by results that apply to updates based on a finite set of samples. First, we characterize the domain of attraction of any global maximizer of the population likelihood. This characterization is based on a novel view of the EM updates as a perturbed form of likelihood ascent, or in parallel, of the gradient EM updates as a perturbed form of standard gradient ascent. Leveraging this characterization, we then provide non-asymptotic guarantees on the EM and gradient EM algorithms when applied to a finite set of samples. We develop consequences of our general theory for three canonical examples of incomplete-data problems: mixture of Gaussians, mixture of regressions, and linear regression with covariates missing completely at random. In each case, our theory guarantees that with a suitable initialization, a relatively small number of EM (or gradient EM) steps will yield (with high probability) an estimate that is within statistical error of the MLE. We provide simulations to confirm this theoretically predicted behavior