Bridge 129 (Italy)

To date, the quantification of the anticoagulant (ACD-A) in plasma units has been based on theoretical calculations. An accurate quantification could help minimize the risks associated with plasmapheresis, given that the total ACD-A used during the procedure is distributed between the donor and the plasma unit. Our aim was to experimentally quantify the volume of ACD-A in units collected by plasmapheresis. We used proton nuclear magnetic resonance spectroscopy to measure the ACD-A volume in 295 plasma units collected by the Azienda USL-IRCCS of Reggio Emilia, Italy. We analyzed the determinants of the differences between estimated and measured ACD-A through multivariate regression models. The experimentally measured ACD-A in plasma units was variable, with 45% of the samples showing a discrepancy of more than 15 mL compared to the manufacturer's estimate. ACD-A was underestimated for higher density of the units (p < 0.0005); a weak association was also observed with triglycerides (underestimated for higher levels, p = 0.015) and sex (overestimated in females, p = 0.008), but our model explained only 35% of the individual variability. The manufacturer's algorithms do not accurately estimate the ACD-A in units collected by plasmapheresis. Donor-related characteristics may affect ACD-A distribution between donor and plasma unit, thereby explaining the discrepancies between estimate and measurement. Errors in the estimate of the ACD-A actually received by donors could hamper studies on dose-response relationship between anticoagulant and adverse reactions. Our work should stimulate research on tailored procedures aimed at minimizing the anticoagulant received by donors and increasing plasmapheresis safety.

New threats to security have appeared in recent years: terrorism, organized crime, trafficking, smuggling, proliferation of weapons of mass destruction, the role of video and the Internet in spreading extremist propaganda is currently highlighted. The reaction to the new threats is slowed for the huge, unmanageable amount of available data about them. To contrast these new threats and their new “weapons”, the research in automatic surveillance technologies (i.e.: able to detect criminal or suspect behaviour without human help) has quickly advanced. Intelligence analysts in their daily fights are profiting of these new surveillance technologies (e.g. advanced CCTV and crawlers, text and video mining) to automatically analyze the content of video data streams, suspected web sites containing videos, blogs, emails, chat lines, instant messages and all other digital media detecting links between people and organizations, trends of social and economic actions, topics of interest also if they are “sunk” among terabytes of information. Examples of application are here presented.