Decreased plasma nociceptin/orphanin FQ levels after acute coronary syndromes.

Foregoing researches made on the N/OFQ system brought up a possible role for this system in cardiovascular regulation. In this study we examined how N/OFQ levels of the blood plasma changed in acute cardiovascular diseases. Three cardiac patient groups were created: enzyme positive acute coronary syndrome (EPACS, n = 10), enzyme negative ACS (ENACS, n = 7) and ischemic heart disease (IHD, n = 11). We compared the patients to healthy control subjects (n = 31). We found significantly lower N/OFQ levels in the EPACS [6.86 (6.21-7.38) pg/ml], ENACS [6.97 (6.87-7.01) pg/ml and IHD groups [7.58 (7.23-8.20) pg/ml] compared to the control group [8.86 (7.27-9.83) pg/ml]. A significant correlation was detected between N/OFQ and white blood cell count (WBC), platelet count (PLT), creatine kinase (CK), glutamate oxaloacetate transaminase (GOT) and cholesterol levels in the EPACS group.Decreased plasma N/OFQ is closely associated with the presence of acute cardiovascular disease, and the severity of symptoms has a significant negative correlation with the N/OFQ levels. We believe that the rate of N/OFQ depression is in association with the level of ischemic stress and the following inflammatory response. Further investigations are needed to clarify the relevance and elucidate the exact effects of the ischemic stress on the N/OFQ system.

Kinetic assay for the determination of the oxidative stress biomarker, advanced oxidation protein products (AOPP) in the human blood plasma.

UNLABELLED: A number of human diseases and pathological conditions were found to be associated with increased oxidative stress. In the literature several techniques are available for the assessment of oxidative stress, but most of them are not applicable for a routine medical laboratory due to the complex methodology and/or financial reasons. We report here on a simple, inexpensive, kinetic assay for the determination of the oxidative stress biomarker, advanced oxidation protein products (AOPP) in the human blood plasma. METHODS: This study involved 70 patients (47M/23F; mean age: 64.6 y; range: 16-85) admitted to our Department with a wide range of cardiovascular and peripheral vascular diseases. Three critically ill patients were assigned for monitoring purposes. Plasma AOPP were simultaneously determined using an end-point assay as reference method and by a kinetic method developed in our laboratory. Plasma fibrinogen concentration was measured according to the Clauss method. RESULTS: There was a highly significant correlation (r2 = 0.588; p < 0.0001) between AOPP concentration (reference method) and AOPP reactivity (kinetic method). Both AOPP concentration and AOPP reactivity also significantly correlated with plasma fibrinogen concentration (r2 = 0.780; p < 0.0001; r2 = 0.564; p < 0.0001). The three representative cases presented appear to support the relevance of our novel method in the monitoring of critically ill patients. CONCLUSIONS: This simple and inexpensive kinetic assay can be widely used in any routine laboratory interested in oxidative stress research. It is especially recommended for monitoring critically ill or other patients.

Reliability of logP predictions based on calculated molecular descriptors: a critical review.

Correct QSAR analysis requires reliable measured or calculated logP values, being logP the most frequently utilized and most important physico-chemical parameter in such studies. Since the publication of theoretical fundamentals of logP prediction, many commercial software solutions are available. These programs are all based on experimental data of huge databases therefore the predicted logP values are mostly acceptable - especially for known structures and their derivatives. In this study we critically reviewed the published methods and compared the predictive power of commercial softwares (CLOGP, KOWWIN, SciLogP/ULTRA) to each other and to our recently developed automatic QS(P)AR program. We have selected a very diverse set of 625 known drugs (98%) and drug-like molecules with experimentally validated logP values. We have collected 78 reported "outliers" as well, which could not be predicted by the "traditional" methods. We used these data in the model building and validation. Finally, we used an external validation set of compounds missing from public databases. We emphasized the importance of data quality, descriptor calculation and selection, and presented a general, reliable descriptor selection and validation technique for such kind of studies. Our method is based on the strictest mathematical and statistical rules, fully automatic and after the initial settings there is no option for user intervention. Three approaches were applied: multiple linear regression, partial least squares analysis and artificial neural network. LogP predictions with a multiple linear regression model showed acceptable accuracy for new compounds therefore it can be used for "in-silico-screening" and/or planning virtual/combinatorial libraries.