Body composition and physical ability affect the life quality and health condition of elderly people. The aim of this study was to evaluate and compare body composition and physical ability of regularly trained and non-trained elderly women. 22 elderly women in the average age 74 years were divided into two groups based on their physical activity: twice a week training and non-training groups. Body weight, body height, blood pressure and waist-hip ratio were measured, and the body composition was evaluated with a SECA 525 analyser. A handgrip strength test, a “Timed Up and Go” test, “Five Times Sit to Stand” test were also performed by the participants. Based on the results of the study, the following conclusions were made: regularly training women had lower body mass index and systolic blood pressure than non-trained women in the same age; regularly training women had lower body fat mass and higher total body water, fat-free mass and muscle mass than non-training women in the same age; regularly training women had greater hand grip strength than nontraining women in the same age; regularly training women performed “Five Times Sit to Stand” test and “Timed Up and Go” test faster than non-training women in the same age.

Performance of COSMO-RS method as a tool for partition and distribution modeling in 20 solvent pairs—composed of neutral or acidic aqueous solution and organic solvents of different polarity, ranging from alcohols to toluene and hexane—was evaluated. Experimental partition/distribution data of lignin-related and drug-like compounds (neutral, acidic, moderately basic) were used as reference. Several aspects of partition modeling were addressed: accounting for mutual saturation of aqueous and organic phases, variability of systematic prediction errors across solvent pairs, taking solute ionization into account. COSMO-RS was found to predict extraction outcome for both ligneous and drug-like compounds in various solvent pairs fairly well without any additional empirical input. The solvent-specific systematic errors were found to be moderate, despite being statistically significant, and related to the solvent hydrophobicity. Accounting for mutual solubilities of the two liquids was proven crucial in cases where water was considerably soluble in the organic solvent. The root mean square error of a priori log P prediction varied, depending mainly on the solvent pair, from 0.2 to 0.7, overall value being 0.6 log units. The accuracy was higher in case of hydrophilic than hydrophobic solvents. The log D predictions were less accurate, due to p K a prediction being an additional source of error, and also because of the complexity of modeling the behaviour of ionic species in the two-phase system. A simple correction for partitioning of free ions was found to notably improve log D prediction accuracy in case of the most hydrophilic organic phase (butanol/water).

We present a systematic approach for predicting the best solvents for selective extraction of components with unknown structure from complex mixtures (e.g., natural products)-a tool promising dramatic simplification of extraction process optimization. Its key advantage is that identification of the component(s) is unnecessary-prediction is based on a small set of experimental distribution coefficients (obtained using a combination of shake-flask extraction and chromatographic analysis) rather than structure-based descriptors. The methodology is suitable for the very common situations in practice where the desired compound needs to be separated from unknown impurities (i.e., selectively extracted from the mixture), as well as for large-scale and high-throughput work. The proof-of-concept methodology was developed and evaluated using an extensive set of experimental distribution data of lignin-related compounds obtained in this work.