A new device for measurement of fibrin clot lysis: application to the Euglobulin Clot Lysis Time

BACKGROUND: Determination of clot lysis times on whole blood, diluted whole blood, plasma or plasma fraction has been used for many years to assess the overall activity of the fibrinolytic system. We designed a completely computerised semi-automatic 8-channel device for measurement and determination of fibrin clot lysis. The lysis time is evaluated by a mathematical analysis of the lysis curve and the results are expressed in minute (range: 5 to 9999). We have used this new device for Euglobulin Clot Lysis Time (ECLT) determination, which is the most common test used in laboratories to estimate plasma fibrinolytic capacity. RESULTS: The correlation between ECLT and manual method is very tight : R = 0,99; p < 10(-6). The efficiency scores of the method are <4% in intra-assay and <7% in inter-assay. It allows to achieve the tests on hyperlipaemic samples. This new device has been easily integrated in laboratory routine and allows to achieve several ECLT every day without disturbance of laboratory workflow. CONCLUSIONS: The routine use of this new device could be useful in various situations such as assessment in atherosclerosis and arteriosclerosis associated diseases, coagulation survey of liver transplantations, cardiovascular surgery or pharmacological research. It has already provided highly promising results in preliminary studies on the relation between fibrinolysis and cardiovascular risk factors

Temporal Dissociation between Myeloperoxidase (MPO)-Modified LDL and MPO Elevations during Chronic Sleep Restriction and Recovery in Healthy Young Men

OBJECTIVES: Many studies have evaluated the ways in which sleep disturbances may influence inflammation and the possible links of this effect to cardiovascular risk. Our objective was to investigate the effects of chronic sleep restriction and recovery on several blood cardiovascular biomarkers. METHODS AND RESULTS: Nine healthy male non-smokers, aged 22-29 years, were admitted to the Sleep Laboratory for 11 days and nights under continuous electroencephalogram polysomnography. The study consisted of three baseline nights of 8 hours sleep (from 11 pm to 7 am), five sleep-restricted nights, during which sleep was allowed only between 1 am and 6 am, and three recovery nights of 8 hours sleep (11 pm to 7 am). Myeloperoxidase-modified low-density lipoprotein levels increased during the sleep-restricted period indicating an oxidative stress. A significant increase in the quantity of slow-wave sleep was measured during the first recovery night. After this first recovery night, insulin-like growth factor-1 levels increased and myeloperoxidase concentration peaked. CONCLUSIONS: We observed for the first time that sleep restriction and the recovery process are associated with differential changes in blood biomarkers of cardiovascular disease

Deciphering Diseases and Biological Targets for Environmental Chemicals using Toxicogenomics Networks

Exposure to environmental chemicals and drugs may have a negative effect on human health. A better understanding of the molecular mechanism of such compounds is needed to determine the risk. We present a high confidence human protein-protein association network built upon the integration of chemical toxicology and systems biology. This computational systems chemical biology model reveals uncharacterized connections between compounds and diseases, thus predicting which compounds may be risk factors for human health. Additionally, the network can be used to identify unexpected potential associations between chemicals and proteins. Examples are shown for chemicals associated with breast cancer, lung cancer and necrosis, and potential protein targets for di-ethylhexyl-phthalate, 2,3,7,8-tetrachlorodibenzo-p-dioxin, pirinixic acid and permethrine. The chemical-protein associations are supported through recent published studies, which illustrate the power of our approach that integrates toxicogenomics data with other data types

Network Analysis Tools: from biological networks to clusters and pathways

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein–protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in B1 h