The interaction between anti-PF4 antibodies and anticoagulants in vaccine-induced thrombotic thrombocytopenia.

Life-threatening thrombotic events at unusual sites have been reported after vector-based vaccinations against severe acute respiratory syndrome coronavirus 2. This phenomenon is now termed vaccine-induced immune thrombotic thrombocytopenia (VITT). The pathophysiology of VITT is similar to that of heparin-induced thrombocytopenia (HIT) and is associated with platelet-activating antibodies (Abs) against platelet factor 4 (PF4). Therefore, current guidelines suggest nonheparin anticoagulants to treat VITT patients. In this study, we investigated the interactions of heparin, danaparoid, fondaparinux, and argatroban with VITT-Ab/PF4 complexes using an ex vivo model for thrombus formation as well as in vitro assays to analyze Ab binding and platelet activation. We found that immunoglobulin Gs (IgGs) from VITT patients induce increased adherent platelets/thrombus formation in comparison with IgGs from healthy controls. In this ex vivo flow-based model, the procoagulant activity of VITT IgGs was effectively inhibited with danaparoid and argatroban but also by heparin. Interestingly, heparin and danaparoid not only inhibited IgG binding to PF4 but were also able to effectively dissociate the preformed PF4/IgG complexes. Fondaparinux reduced the in vitro generation of procoagulant platelets and thrombus formation; however, it did not affect platelet aggregation. In contrast, argatroban showed no effect on procoagulant platelets and aggregation but significantly inhibited VITT-mediated thrombus formation. Taken together, our data indicate that negatively charged anticoagulants can disrupt VITT-Ab/PF4 interactions, which might serve as an approach to reduce Ab-mediated complications in VITT. Our results should be confirmed, however, in a clinical setting before a recommendation regarding the selection of anticoagulants in VITT patients could be made.

Left Frontal EEG Power Responds to Stock Price Changes in a Simulated Asset Bubble Market.

Financial bubbles are a result of aggregate irrational behavior and cannot be explained by standard economic pricing theory. Research in neuroeconomics can improve our understanding of their causes. We conducted an experiment in which 28 healthy subjects traded in a simulated market bubble, while scalp EEG was recorded using a low-cost, BCI-friendly desktop device with 14 electrodes. Independent component (IC) analysis was performed to decompose brain signals and the obtained scalp topography was used to cluster the ICs. We computed single-trial time-frequency power relative to the onset of stock price display and estimated the correlation between EEG power and stock price across trials using a general linear model. We found that delta band (1-4 Hz) EEG power within the left frontal region negatively correlated with the trial-by-trial stock prices including the financial bubble. We interpreted the result as stimulus-preceding negativity (SPN) occurring as a dis-inhibition of the resting state network. We conclude that the combination between the desktop-BCI-friendly EEG, the simulated financial bubble and advanced signal processing and statistical approaches could successfully identify the neural correlate of the financial bubble. We add to the neuroeconomics literature a complementary EEG neurometric as a bubble predictor, which can further be explored in future decision-making experiments.