Lack of influence of the COX inhibitors metamizol and diclofenac on platelet GPIIb/IIIa and P-selectin expression in vitro

BACKGROUND: The effect of non-steroidal anti-inflammatory drugs (NSAIDs) for reduced platelet aggregation and thromboxane A(2 )synthesis has been well documented. However, the influence on platelet function is not fully explained. Aim of this study was to examine the influence of the COX-1 inhibiting NSAIDs, diclofenac and metamizol on platelet activation and leukocyte-platelet complexes, in vitro. Surface expression of GPIIb/IIIa and P-selectin on platelets, and the percentage of platelet-leukocyte complexes were investigated. METHODS: Whole blood was incubated with three different concentrations of diclofenac and metamizol for 5 and 30 minutes, followed by activation with TRAP-6 and ADP. Rates of GPIIb/IIIa and P-selectin expression, and the percentage of platelet-leukocyte complexes were analyzed by a flow-cytometric assay. RESULTS: There were no significant differences in the expression of GPIIb/IIIa and P-selectin, and in the formation of platelet-leukocyte complexes after activation with ADP and TRAP-6, regarding both the time of incubation and the concentrations of diclofenac and metamizol. CONCLUSIONS: Accordingly, the inhibitory effect of diclofenac and metamizol on platelet aggregation is not related to a reduced surface expression of P-selectin and GPIIb/IIIa on platelets

Engineered nanomaterials: toward effective safety management in research laboratories

It is still unknown which types of nanomaterials and associated doses represent an actual danger to humans and environment. Meanwhile, there is consensus on applying the precautionary principle to these novel materials until more information is available. To deal with the rapid evolution of research, including the fast turnover of collaborators, a user-friendly and easy-to-apply risk assessment tool offering adequate preventive and protective measures has to be provided.Results: Based on new information concerning the hazards of engineered nanomaterials, we improved a previously developed risk assessment tool by following a simple scheme to gain in efficiency. In the first step, using a logical decision tree, one of the three hazard levels, from H1 to H3, is assigned to the nanomaterial. Using a combination of decision trees and matrices, the second step links the hazard with the emission and exposure potential to assign one of the three nanorisk levels (Nano 3 highest risk; Nano 1 lowest risk) to the activity. These operations are repeated at each process step, leading to the laboratory classification. The third step provides detailed preventive and protective measures for the determined level of nanorisk.Conclusions: We developed an adapted simple and intuitive method for nanomaterial risk management in research laboratories. It allows classifying the nanoactivities into three levels, additionally proposing concrete preventive and protective measures and associated actions. This method is a valuable tool for all the participants in nanomaterial safety. The users experience an essential learning opportunity and increase their safety awareness. Laboratory managers have a reliable tool to obtain an overview of the operations involving nanomaterials in their laboratories; this is essential, as they are responsible for the employee safety, but are sometimes unaware of the works performed. Bringing this risk to a three-band scale (like other types of risks such as biological, radiation, chemical, etc.) facilitates the management for occupational health and safety specialists. Institutes and school managers can obtain the necessary information to implement an adequate safety management system. Having an easy-to-use tool enables a dialog between all these partners, whose semantic and priorities in terms of safety are often different