Effect of NaCl additive on solute-solvent interactions in aqueous polyethylene glycol-sodium sulfate two-phase systems

Abstract Partition behavior of eight small organic compounds and six proteins was examined in poly(ethylene glycol)-8000-sodium sulfate aqueous two-phase systems containing 0.215 M NaCl and 0.5 M osmolyte (sorbitol, sucrose, TMAO) and poly(ethylene glycol)-10000-sodium sulfate-0.215 M NaCl system, all in 0.01 M sodium phosphate buffer, pH 6.8. The differences between the solvent properties of the coexisting phases (solvent dipolarity/polarizability, hydrogen bond donor acidity, and hydrogen bond acceptor basicity) were characterized with solvatochromic dyes using the solvatochromic comparison method. Differences between the electrostatic properties of the phases were determined by analysis of partitioning of sodium salts of dinitrophenylated (DNP-) amino acids with aliphatic alkyl side-chain. The partition coefficients of all compounds examined (including proteins) were described in terms of solute-solvent interactions. The results obtained in the study show that solute-solvent interactions of nonionic organic compounds and proteins in polyethylene glycol-sodium sulfate aqueous two-phase system change in the presence of NaCl additive.

Is honey able to potentiate the antioxidant and cytotoxic properties of medicinal plants consumed as infusions for hepatoprotective effects?

Due to the enormous variety of phytochemicals present in plants, their extracts have been used for centuries in the treatment of innumerous diseases, being perceived as an invaluable source of medicines for humans. Furthermore, the combination of different plants was reported as inducing an improved effect (synergism) in comparison to the additive activity of the plants present in those mixtures. Nevertheless, information regarding the effects of plant infusions added with honey is still rather scarce. Accordingly, the aim of this study was evaluating the interaction between chestnut honey, a natural product with well-reported beneficial properties, and three medicinal plants (either as single plant or as combinations of two and three plants), with regard to their antioxidant activity and hepatotoxicity. Antioxidant activity was evaluated by comparing the results from four different assays; the hepatotoxicity was assessed in two different cell lines. Results were compared by analysis of variance and linear discriminant analysis. The addition of honey to the infusions had a beneficial result in both cases, producing a synergistic effect in all samples, except beta-carotene bleaching inhibition for artichoke+milk thistle+honey preparation and also preparations with lower hepatotoxicity, except in the case of artichoke+honey. Moreover, from discriminant linear analysis output, it became obvious that the effect of honey addition overcame that resulting from using single plant or mixed plants based infusions. Also, the enhanced antioxidant activity of infusions containing honey was convoyed by a lower hepatotoxicity.

Predicting SO2 pollution incidents by means of additive models with optimum variable selection

The aim of this paper is to predict time series of SO2 concentrations emitted by coal-fired power stations in order to estimate in advance emission episodes and analyze the influence of some meteorological variables in the prediction. An emission episode is said to occur when the series of bi-hourly means of SO2 is greater than a specific level. For coal-fired power stations it is essential to predict emission epi- sodes sufficiently in advance so appropriate preventive measures can be taken. We proposed a meth- odology to predict SO2 emission episodes based on using an additive model and an algorithm for variable selection. The methodology was applied to the estimation of SO2 emissions registered in sampling lo- cations near a coal-fired power station located in Northern Spain. The results obtained indicate a good performance of the model considering only two terms of the time series and that the inclusion of the meteorological variables in the model is not significant.