Studies of polyamines transport through liquid membranes with D2EHPA as a carrier

Determination of polyamines in urine via electrospun nanofibers-based solid-phase extraction coupled with GC-MS and application to gastric cancer patients

The simultaneous determination of polyamines and their metabolites in urine samples was achieved by gas chromatography-mass spectrometry in the selected ion monitoring mode. After conjugating with the ion-pair reagent bis-2-ethylhexylphosphate in the aqueous phase, the polyamines in the samples were extracted with polystyrene nanofiber-based packed-fiber solid-phase extraction followed by a derivatization step using pentafluoropropionyl anhydride. With optimal conditions, all analytes were separated well. For analytes of putrescine, cadaverine, N-acetylputrescine, and N-acetylcadaverine, the linearity was good in the range of 0.05-500 μmol/L (R²  ≥ 0.993). While for spermidine, spermine, acetylspermidine, N⁸ -acetylspermidine, and N-acetylspermine, the linearity was good in the range of 0.5-500 μmol/L (R²  ≥ 0.990). The recoveries of three spiked concentrations (0.5, 5, 300 μmol/L) were 85.6%-108.4%, and relative standard deviations for intra- and interday were in the range of 2.9%-13.4% and 4.5%-15.1%, respectively. The method was successfully applied to the analysis of urine samples of gastric cancer patients. The results showed that the levels of most polyamines and N-acetylated polyamines from the patient group were significantly higher than those from the control group. The altered concentrations of the above-mentioned metabolites suggest their role in the pathogenesis of gastric cancer, and they should be further evaluated as potential markers of gastric cancer.

Prediction of extraction efficiency in supported liquid membrane with a stagnant acceptor phase by means of artificial neural network

An artificial neural network model of supported liquid membrane extraction process with a stagnant acceptor phase is proposed. Triazine herbicides and phenolic compounds were used as model compounds. The model is able to predict the compound extraction efficiency within the same family based on the octanol-water partition coefficient, water solubility, molecular mass and ionisation constant of the compound. The network uses the back-propagation algorithm for evaluating the connection strengths representing the correlations between inputs (octanol-water partition coefficients logP, acid dissociation constant pK(a), water solubility and molecular weight) and outputs (extraction efficiency in dihexyl ether and undecane as organic solvents). The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be smaller than ±3%. Moreover, standard statistical methods were applied for exploration of relationships between studied parameters.