Uptongreer9325

Hydrolysates containing angiotensin I-converting enzyme (ACE)-inhibitory peptide were prepared from protein of Alaska pollack skins using alcalase and trypsin. The protein hydrolysate was separated by ultrafiltration, Sephadex G-25 gel filtration chromatography and reversed phase high-performance liquid chromatography (HPLC), from which a novel purified peptide was obtained. Both random coil structure and β-sheet in the purified peptide were revealed in Fourier transform infrared spectrum. Mereletinib The amino sequence of the purified peptide was identified as GPLGVP, VLYPVK, VFLENVLR, and FEEF by HPLC-Q-TOF-MS (HPLC-quadrupole time-of-flight mass spectrometry). The peptide GPLGVP whose molecular weight was 538.31 Da showed the highest ACE inhibitory activity (IC50 = 105.8 µM). The purified peptide featured a noncompetitive inhibition kinetic mechanism was shown in the Lineweaver-Burk plots and was susceptible to enzymes as indicated in the studies on stability of gastrointestinal proteases. Moreover, the peptide GPLGVP can combine ACE catalytic pocket through hydrogen bonds and other forces with high binding power as disclosed in molecular docking simulation, which provides the inhibitory effect of GPLGVP on ACE.

To describe the relationship between leadership orientation and emotional intelligence levels of nursing students.

The study is a cross-sectional and descriptive correlational study.

This study was carried out with 320 nursing students. There was a positive relationship between the mean scores for the Leadership Orientations subdimensions and the mean scores for the overall Emotional Intelligence Evaluation Scale and its subdimensions.

More studies are needed to examine the relationship between students' emotional intelligence and leadership orientations.

More studies are needed to examine the relationship between students' emotional intelligence and leadership orientations.In this study, the feasibility of preparing oxhide gelatin from cowhide scrap by high pressure assisted-liquid extraction was verified. Different processing conditions, including high pressure time (15 to 25 min), pressure (250 to 350 MPa), and liquid-to-solid ratio (13 to 15), were optimized through response surface methodology. Under the optimum manufacturing conditions, when the high-pressure processing (HPP) time was 22 min, the pressure was 289 MPa, and the liquid-to-solid ratio was 14, the highest extraction yield (36%) and gel strength (224 g) were achieved. Based on DSC, XRD, FTIR, SEM, gelling and melting temperatures, HPP led to the structural modification of the gelatinized collagen, which enhanced the rearrangement of the gel structure during the gelation process and made it have better gelling properties. In addition, compared with the commercial sample, they do not differ significantly in the relaxation time and peak area of prepared oxhide gelatin. These findings provide new insights into the practicability of HPP during the preparation of oxhide gelatin, which can noticeably reduce the processing time and be applied to industrial production. PRACTICAL APPLICATION Compared with traditional processing, a high pressure-assisted extraction process can noticeably reduce the processing time while producing cowhide gelation with similar physicochemical and functional properties. Meanwhile, high pressure processing (HPP) led to the structural destruction of the cowhide and gelatinized collagen, which enhanced the rearrangement of the gelatin structure during the gelation process and made it have better gelling properties. Importantly, high pressure-assisted extraction can facilitate the use of a low-cost raw material and improve the preparation efficiency of oxhide gelatin, which shows great potential in large-scale and efficient industrial production and the quality control of oxhide gelatin.Effectsof microwave vacuum drying (MVD) on moisture migration, microstructure, and rehydration of sea cucumber were investigated in this paper. Vacuum condition avoided the exposure of sea cucumber to high temperature. Low-field nuclear magnetic resonance relaxation results revealed that the peaks of three water components in sea cucumber shifted to short relaxation time during MVD process, and the peak area of major water component-immobilized water-decreased significantly due to water evaporation. Magnetic resonance imaging found that the water in the internal layer of sea cucumber body wall was first removed due to the internal heating of microwave, and then the water in the outer layer. Higher microwave power could promote the moisture transfer motion during drying process, and shorten the drying time. Porous microstructure was observed by Cryo scanning electronic microscope images in sea cucumber dried with microwave power of 200 and 250 W, which might be responsible for high values of rehydration ratio and water holding capacity. High microwave power caused the increase of amino acids content, but had no significant effect on the change of saponins content. In addition, excellent prediction models of moisture ratio have been developed by partial least squares regression analysis based on transverse relaxation data, which proved the feasibility of low-field nuclear magnetic resonance to monitor moisture changes of sea cucumber during MVD process. PRACTICAL APPLICATION Effects of microwave vacuum drying (MVD) on moisture migration, microstructure, and rehydration of sea cucumber were investigated. Understanding the impacts of MVD drying on water status, texture, and nutritional characteristics of sea cucumber is important to improve the processing quality of dried sea cucumber.

Although the main method for authentication of monofloral honey is pollen analysis, other classification approaches have been also applied. However, the majority of the existing classification models so far have utilized a few honey types or a few honey samples of each honey type, which can lead to inaccurate results. Aiming at addressing this, the goal of the present study was to create a classification model by analysing in total 250 honey samples from 15 different monofloral honey types in ten physicochemical parameters and then, multivariate analysis [multivariate analysis of variance (MANOVA), principal component analysis (PCA) and multi-discriminant analysis (MDA)] was applied in an effort to distinguish and classify them.

Electrical conductivity and colour were found to have the highest discriminative power, allowing the classification of monofloral honey types, such as oak, knotgrass and chestnut honey, as well as the differentiation between honeydew and nectar honeys. The classification model had a high predictive power, as the 84.