Adaptation of Conductometric Monoenzyme Biosensor for Rapid Quantitative Analysis of L-arginine in Dietary Supplements.

The present study reports on the development, adaptation, and optimization of a novel monoenzyme conductometric biosensor based on a recombinant arginine deiminase (ADI) for the determination of arginine in dietary supplements with a high accuracy of results. Aiming for the highly sensitive determination of arginine in real samples, we studied the effect of parameters of the working buffer solution (its pH, buffer capacity, ionic strength, temperature, and protein concentration) on the sensitivity of the biosensor to arginine. Thus, it was determined that the optimal buffer is a 5 mM phosphate buffer solution with pH 6.2, and the optimal temperature is 39.5 °C. The linear functioning range is 2.5-750 µM of L-arginine with a minimal limit of detection of 2 µM. The concentration of arginine in food additive samples was determined using the developed ADI-based biosensor. Based on the obtained results, the most effective method of biosensor analysis using the method of standard additions was chosen. It was also checked how the reproducibility of the biosensor changes during the analysis of pharmaceutical samples. The results of the determination of arginine in real samples using a conductometric biosensor based on ADI clearly correlated with the data obtained using the method of ion-exchange chromatography and enzymatic spectrophotometric analysis. We concluded that the developed biosensor would be effective for the accurate and selective determination of arginine in dietary supplements intended for the prevention and/or elimination of arginine deficiency.

Statistically verified methods for determining predictors of development of arterial hypertension depending on endothelial nitric oxide synthase T786C gene promoter polymorphism using lipid profile indicators.

Objective. Polymorphism investigation of T786C gene promoter of endothelial nitric oxide synthase (eNOS/NOS3) in the arterial hypertension is a promising field for determining the relationship between heredity, hypertension, and dyslipidemia, which still remains controversial. The purpose of the study was to investigate the lipid profile, which depends on the NOS3 T786C gene promotor region polymorphism in patients with arterial hypertension. Methods. The study involved 86 patients with arterial hypertension. The control group consisted of 30 basically healthy individuals. The lipid profile in the blood serum of the studied patients was measured by commercially available kits using Biochem FC-200 analyzer (HTI, USA). The allelic polymorphism of NOS3 T786C gene promoter was studied using a polymerase chain reaction technique with electrophoretic detection of the results. Results. An increase at the level of all atherogenic fractions in the blood was found in the group of patients carrying the CC genotype compared with carriers of the TT genotype of the NOS3 gene. The total cholesterol serum level in the group of carriers of the CC genotype of NOS3 T786C gene promoter increased by 33.3% compared with carriers of the TT genotype and it was almost twice as high as the control values. In the group of carriers in the CC genotype of the NOS3 gene, the serum level of triglycerides was statistically significantly higher (2.9 times) than in the group of carriers of the TT genotype. The low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL) serum levels significantly increased in patients with arterial hypertension with the CC genotype by 1.6 and 4.6 times, respectively, compared with the TT genotype carriers. The high-density lipoprotein (HDL) serum level, as an antiatherogenic factor, was statistically significantly lower (by 45.8%) in the group of the CC genotype carriers of the NOS3 gene than in the group with carriers of the TT genotype (0.58±0.06 vs. 1.07±0.03 mmol/l.) Conclusions. The increase in all atherogenic and decrease in antiatherogenic lipid parameters of the lipidogram of patients with arterial hypertension and the deepening of dyslipidemia in carriers of the CC genotype compared with carriers of the TT genotype of the NOS3 T786C gene promoter is crucial in the development of dyslipidemia.

Application of roc-analysis to assess the quality of predicting the risk of chronic rhinosinusitis recurrence.

OBJECTIVE: Aim: To propose a new, original approach to assessing the quality of a multivariate regression model for predicting the risk of recurrence in patients with chronic rhinosinusitis based on ROC analysis with the construction of appropriate curves, estimating the area under them, as well as calculating the sensitivity, accuracy, specificity, and predictive value of a positive and negative classification results, the likelihood ratio of positive and negative patient detection results. PATIENTS AND METHODS: Materials and Methods: 204 patients aged with a diagnosis of chronic rhinosinusitis were examined. RESULTS: Results: To build a multivariate regression model 14 probable factors of chronic rhinosinusitis occurrence were selected to determine the diagnostic value of the proposed model we calculate the sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), the likelihood ratio of a positive test (LR+), the likelihood ratio of a negative test (LR-) and prediction accuracy % of the proposed mathematical model. In order to determine the prognostic value of the risk ratio of CRS recurrence model, ROC- analysis was performed, ROC curves were obtained. CONCLUSION: Conclusions: The multivariate regression model makes it possible to predict potential complications and the possibility of disease recurrence. The construction of ROC-curves allows us to assert the excellent classification quality of chronic rhinosinusitis recurrence.

MULTIFACTOR REGRESSION MODEL FOR PREDICTION OF CHRONIC RHINOSINUSITIS RECURRENCE.

OBJECTIVE: The aim: To propose an approach to forecasting the risk of chronic rhinosinusitis recurrence based on multivariate regression analysis for effective diagnosis and carrying out treatment and preventive measures. PATIENTS AND METHODS: Materials and methods: 104 patients aged 18 to 80, including 58 women and 46 men, diagnosed with chronic rhinosinusitis were examined. RESULTS: Results: To build a multifactorial regression model for predicting the recurrence of chronic rhinosinusitis, probable factors of the occurrence of the disease were selected. 14 possible factors were analyzed using multivariate regression analysis. 13 risk factors were selected for predicting recurrence of chronic rhinosinusitis with a significance level of less than 0.05. Histograms of the residual deviations of predicting the recurrence of chronic rhinosinusitis were obtained, which are distributed symmetrically, and a normal-probability straight line is presented, on which there are no systematic deviations. The given results confirm the statistical hypothesis that the residual deviations correspond to the normal distribution law. Residual deviations relative to the predicted values are scattered chaotically, which indicates the absence of dependence on the predicted values of the risk of recurrence of chronic rhinosinusitis. The value of the coefficient of determination was calculated, which is 0.988, which gives grounds to claim that 98.8% of the factors are taken into account in the model for predicting the recurrence of chronic rhinosinusitis and its high reliability and acceptability in general. CONCLUSION: Conclusions: The proposed model makes it possible to predict in advance potential complications and the possibility of recurrence of the studied disease.

Prediction of climacteric syndrome development in perimenopausal women with hypothyroidism.

Introduction: The study aim was to predict the risk of climacteric syndrome (CS) developing in perimenopausal women with hypothyroidism (HT) according to the developed algorithm and mathematical model for timely preventive measures. Material and methods: 146 perimenopausal women with autoimmune HT were enrolled in this study. Assessment of the severity of metabolic, neurovegetative and psychoemotional symptoms was graded according to the Blatt-Kupperman menopause index. All women were interviewed according to a specially designed questionnaire for predicting the development of severe CS. Multiple regression analysis was used to build a multifactorial mathematical model. Shapiro-Wilk and Kolmogorov-Smirnov criteria were used to assess the normality of the distribution of traits. Results: Regression analysis was used to determine the most significant multicollinear risk factors for CS developing: pathology of the thyroid gland, smoking, alcohol consumption, adverse environmental conditions, low physical activity, history of stress and anxiety. The predicted value of the risk factor for severe CS with a high degree of probability was determined in 72 (49.32%) women, medium probability in 58 (39.73%) women, and low probability in 16 (10.95%) women. Conclusions: The developed algorithm and mathematical model are informative and allow one to prevent CS and its complications. The decay of women's health starts many years before menopause and prevention of its consequences is an important task for the clinicians.