Developing an analytical method for quantification of trientine based on modified silver nanoparticles.

Trientine or (N,N´-bis(2-aminoethyl)-1,2-ethanediamine (TETA) is a copper chelator and used in Wilson's disease, is aliphatic amine that does not have UV absorbing groups. In this study, the modified silver nanoparticles (AgNPs) by sodium lauryl sulfate have been used to develop an analytical method for quantification of TETA. Different concentrations of TETA were added into a particular concentration of AgNPs and absorbance of each sample was measured at 397 nm under the optimal conditions which include pH, time, salt and AgNPs volume. It was optimized by a design of experiments using response surface methodology. Then, the calibration curve was obtained based on the concentrations of TETA solution versus decrease in the absorbance of AgNPs. Selectivity of the developed method was performed in plasma and presence of common cations i.e. copper, zinc and ferrous. Under optimum conditions, linear range of this method was between 10 and 40 ng.mL- 1 with correlation coefficient (R2) of 0.996 with limit of detection and quantification of 3 ng.mL- 1 and 10 ng.mL- 1, respectively. Selectivity of established method in presence of cations eliminated by diluting because of high sensitivity of the established analytical techniques based on AgNPs. This method is suitable and low costing for quantification of TETA and does not require high equipment.

Fuzzy cognitive map based approach for determining the risk of ischemic stroke.

Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non-linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10-fold cross-validation, for 110 real cases, and the results were compared with those of support vector machine and K-nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 ± 4.5)%. The data used in this study is available by emailing the first author for academic and non-commercial purposes.