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The phase diagrams for carbamazepine (CBZ) and nicotinamide (NCT) in ethanol and ethanol/ethyl acetate mixture were constructed at 298.15 K and 313.15 K under atmospheric pressure using the static method. The solubility of the 1:1 cocrystal is correlated as a function of the cocrystal former (nicotinamide) concentration by a mathematical model based on solubility product theory and the complexation process. The phase diagram of a CBZ–NCT cocrystal in pure ethanol at 298.15 K is asymmetric, which leads to an excess of coformer being needed to isolate the cocrystal. In terms of this issue, it was found that the solvent mixture can have a significant effect on the symmetry of phase diagrams. Employing solvent mixtures could make the phase diagrams of the CBZ–NCT cocrystal more symmetric and enlarge the safe operation region for the cocrystal. Additionally, in the cocrystal formation process, the nucleation of cocrystal and carbamazepine in a slurry crystallization, is a competitive relationship. For avoiding single component crystallization, the supersaturation ratio of cocrystal to drug should be increased to inhibit carbamazepine nucleation. These findings are all beneficial to the development of the cocrystallization process of the CBZ–NCT cocrystal. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is an irreversible neurodegenerative disease that has serious impacts on patients' lives. To provide timely accurate treatment and delay the deterioration of the disease, the accurate early diagnosis and clinical score prediction of PD are extremely important. Differing from previous studies on PD, we propose a network combining feature selection method with feature learning to obtain the most discriminative feature representation for longitudinal early diagnosis and clinical score prediction. Specifically, we first preprocess the multi-modal neuroimaging data at multiple time points to extract original longitudinal multi-modal features. Then, the feature selection method is performed to preliminary reduce the feature dimensions. Finally, the stacked sparse nonnegative autoencoder (SSNAE) is employed to obtain more discriminative longitudinal multi-modal features to improve the accuracy of early diagnosis and clinical score prediction at multiple time points. To verify our proposed network, diverse and extensive experiments are performed on the Parkinson's Progression Markers Initiative dataset, which aims to identify biological markers of PD risk, onset and progression. The results demonstrate that our proposed method is more efficient and achieves promising performance on both longitudinal early diagnosis and clinical scores prediction compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The solubility of N,N′-Bis(2,2,6,6-tetramethyl-4-piperidinyl)-1,6-hexanediamine in seven pure solvents (acetonitrile, acetone, methyl acetate, ethyl acetate, propyl acetate, isopropyl acetate and isobutyl acetate) and two binary solvent mixtures (acetone + acetonitrile and methyl acetate + acetonitrile) were measured from 273.15 to 303.15 K at atmospheric pressure by a dynamic method. The solubility data in these pure solvents were correlated by the modified Apelblat model, the Wilson model and the NRTL model, and that in the binary solvents mixture were fitted to the CNIBS/R-K model and the NRTL model. Furthermore, the mixing thermodynamic properties in pure and binary solvent systems were calculated and are discussed, based on the NRTL model. Finally, the applicability of the model of Zhang et al. (Ind Eng Chem Res 51:6933-6938, 2012) in correlating solubility data versus dielectric constant was extended from organic solvent-water mixtures to pure organic solvents and nonaqueous organic solvent mixtures. It was found that the dissolution behavior of a compound in the binary solvent mixtures can be predicted to some extent from those in pure solvents. [ABSTRACT FROM AUTHOR]