Do Aspirin and Other Antiplatelet Drugs Reduce the Mortality in Critically Ill Patients?

Platelet activation has been implicated in microvascular thrombosis and organ failure in critically ill patients. In the first part the present paper summarises important data on the role of platelets in systemic inflammation and sepsis as well as on the beneficial effects of antiplatelet drugs in animal models of sepsis. In the second part the data of retrospective and prospective observational clinical studies on the effect of aspirin and other antiplatelet drugs in critically ill patients are reviewed. All of these studies have shown that aspirin and other antiplatelet drugs may reduce organ failure and mortality in these patients, even in case of high bleeding risk. From the data reviewed here interventional prospective trials are needed to test whether aspirin and other antiplatelet drugs might offer a novel therapeutic option to prevent organ failure in critically ill patients

A Computational Approach to Negative Priming

Priming is characterized by a sensitivity of reaction times to the sequence of stimuli in psychophysical experiments. The reduction of the reaction time observed in positive priming is well-known and experimentally understood [Scarborough et al., 1977]. Negative priming – the opposite effect – is experimentally less tangible [Fox, 1995]. The dependence on subtle parameter changes (such as response-stimulus interval) usually varies. The sensitivity of the negative priming effect bears great potential for applications in research in fields such as memory, selective attention, and aging effects. We develop and analyze a computational realization, CISAM, of a recent psychological model for action decision making, the ISAM [Kabisch, 2003], which is sensitive to priming conditions. With the dynamical systems approach of the CISAM, we show that a single adaptive threshold mechanism is sufficient to explain both positive and negative priming effects. This is achieved by comparing results obtained by the computational modeling with experimental data from our lab. The implementation provides a rich base from which testable predictions can be derived, e.g. with respect to hitherto untested stimulus-combinations (e.g. single-object trials)