Presepsin did not outperform traditional sepsis biomarkers in diagnosing sepsis from SIRS and in prognostication of mortality in critically ill patients. Presepsin may have a limited adjunct value for both diagnosis and an early risk stratification, performing independently of clinical illness severity.

The polydactylous rat strain (PD/Cub) is a highly inbred (F Ͼ 90) genetic model of metabolic syndrome. The aim of this study was to analyze the genetic architecture of the metabolic derangements found in the PD/Cub strain and to assess its dynamics in time and in response to diet and medication. We derived a PD/Cub ϫ BN/Cub (Brown Norway) F2 intercross population of 149 male rats and performed metabolic profiling and genotyping and multiple levels of genetic linkage and statistical analyses at five different stages of ontogenesis and after high-sucrose diet feeding and dexamethasone administration challenges. The interval mapping analysis of 83 metabolic and morphometric traits revealed over 50 regions genomewide with significant or suggestive linkage to one or more of the traits in the segregating PD/Cub ϫ BN/Cub population. The multiple interval mapping showed that, in addition to "single" quantitative train loci, there are more than 30 pairs of loci across the whole genome significantly influencing the variation of particular traits in an epistatic fashion. This study represents the first whole genome analysis of metabolic syndrome in the PD/Cub model and reveals several new loci previously not connected to the genetics of insulin resistance and dyslipidemia. In addition, it attempts to present the concept of "dynamic genetic architecture" of metabolic syndrome attributes, evidenced by shifts in the genetic determination of syndrome features during ontogenesis and during adaptation to the dietary and pharmacological influences.quantitative trait loci; pharmacogenetics; nutrigenetics; triglyceride; insulin resistance; obesity METABOLIC SYNDROME is a complex condition arising from an intricate network of interacting genetic and environmental factors. The dynamics of the increase in its worldwide prevalence qualify the syndrome as a major healthcare issue for years to come. The syndrome comprises several clinical features, each representing a complex trait of its kind, i.e., hypertriglyceridemia, hyperinsulinemia, insulin resistance, obesity, and hypertension (11,36). It is therefore self evident that detailed analysis of the genetic determinants underlying such metabolic clustering is rather complicated in the general human population and only somewhat more feasible in particular circumstances, e.g., in population isolates (32). Although some progress has been made and several studies succeeded in identification of genes potentially involved in the pathogenesis of metabolic syndrome (13,17,35,51), its genetic determination is far from being fully understood.As in other complex diseases, defined animal models of metabolic syndrome are proving to be important tools for deciphering the causative genes and gene-gene and geneenvironment interactions (8). There are numerous rodent strains expressing all or a subset of metabolic syndrome attributes found in humans [e.g., Otsuka Long-Evans Tokushima Fatty rat (16), Goto-Kakizaki rat (10), hereditary hypertriglyceridemic rat (53, 54), and spontaneously hypertensive rat ...

Background/Aims: The determination of neuron-specific enolase (NSE) is relatively frequently requested in the differential diagnosis of small-cell lung carcinoma and non-small-cell lung carcinoma. The individual results of different immunoassays are often not comparable, which has been confirmed by long-term external quality assessments. In this study, we assessed the possible sources of these differences. Methods: More than 3,000 NSE analyses were performed using seven different immunoassays: DELFIA (PerkinElmer), Elecsys 2010 or Modular Analytics E 170 (Roche), Kryptor (B.R.A.H.M.S.), the enzyme-linked immunosorbent assay DRG and three assays based on immunoradiometric assays (DiaSorin, Immunotech and Schering-CIS). The following parameters were evaluated: precision profile of the individual methods, linearity on dilution and modified recovery, comparability and discrimination of immunoassays, sensitivity, and specificity. Results: There were differences in the correlation of values of certain low-concentration specimens. Some assays correlate well while others do not (up to fivefold difference), especially in the case of controls prepared synthetically. Therefore, the current non-standardized preparation of controls is questionable in our opinion. In the cutoff range, the difference in the results of native samples did not exceed its double value. The variation in values >100 µg/l obtained with different assays is <40%. Conclusion: Our results confirmed expected matrix interferences especially in the range of normal and cutoff NSE concentrations. Another source of discrepancies can be attributed to different antibody affinity to αγ- and γγ-enolase isoenzymes. Finally, improper settings of cutoff values also contribute to the different discrimination of the methods.

Our findings suggest a more complex relationship among advanced glycation, oxidative stress and metabolism of ethanol and their link to nutrition and nutrition-associated parameters. AGE as a result of oxidative stress might be similarly linked to increased cardiovascular risk of heavy alcohol drinkers, as are malnutrition and inflammation; however, further studies are needed to confirm this hypothesis.