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The interaction between human serum albumin (HSA) and apremilast (APR), a novel antipsoriatic drug, was characterized by multimodal analytical techniques including high-performance liquid chromatography (HPLC), fluorescence spectroscopy and molecular docking for the first time. Using an HSA chiral stationary phase, the APR enantiomers were well separated, indicating enantioselective binding between the protein and the analytes. The influence of chromatographic parameters—type and concentration of the organic modifier, buffer type, pH, ionic strength of the mobile phase, flow rate and column temperature—on the chromatographic responses (retention factor and selectivity) was analyzed in detail. The results revealed that the eutomer S-APR bound to the protein to a greater extent than the antipode. The classical van ’t Hoff method was applied for thermodynamic analysis, which indicated that the enantioseparation was enthalpy-controlled. The stability constants of the protein–enantiomer complexes, determined by fluorescence spectroscopy, were in accordance with the elution order observed in HPLC (KR-APR-HSA = 6.45 × 103 M−1, KS-APR-HSA = 1.04 × 104 M−1), showing that, indeed, the later-eluting S-APR displayed a stronger binding with HSA. Molecular docking was applied to study and analyze the interactions between HSA and the APR enantiomers at the atomic level. It was revealed that the most favored APR binding occurred at the border between domains I and II of HSA, and secondary interactions were responsible for the different binding strengths of the enantiomers.

Despite the practical importance of accurate long-term electricity price forecast with high resolution - and the significant need for that - only small percentage of the tremendous papers on energy price forecast attempted to target this topic. Its reason can be the high volatility of electricity prices and the hidden – and often unpredictable – relations with its influencing factors. In our research, we performed different experiments to predicate hourly Hungarian electricity prices using deep neural networks, for short-term and long-term, too. During this work, investigations were made to compare the results of different network structures and to determine the effect of some environmental factors (meteorologic data and date/time - beside the historical electricity prices). Our results were promising, mostly for short-term forecasts - especially by using a deep neural network with one ConvLSTM encoder.

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