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旨在为大豆蛋白在冷冻发酵面团中的应用提供一定的理论依据，对添加大豆分离蛋白（SPI）、质构化大豆分离蛋白（TSP）、水解大豆分离蛋白(SPH)的冷冻发酵面团馒头及冷冻发酵面团进行研究，分析了添加不同种类大豆蛋白的冷冻发酵面团馒头的理化指标（比容、水分含量、pH），并通过测定冷冻发酵面团的二硫键含量、蛋白质二级结构和微观结构，探讨不同种类大豆蛋白对冷冻发酵馒头品质的影响机制。结果表明：添加SPH的发酵面团冷冻储藏前后所蒸制的馒头比容变化最小，在冷冻储藏40 d时蒸制的馒头比容相比冷冻储藏10 d增加了4.59%；同一冷冻储藏时间下添加SPH的发酵面团蒸制的馒头水分含量最低；添加大豆蛋白可以减小在冷冻储藏过程中发酵面团馒头pH的下降速率，至少可延长冷冻发酵面团10 d的保质期；通过傅里叶红外光谱和激光共聚焦对冷冻发酵面团蛋白质结构分析，得出添加3种大豆蛋白均会降低面筋蛋白网络结构的均匀性和连续性，SPI和TSP对面筋蛋白网络结构的影响小于SPH，但在冷冻储藏过程中添加SPH的发酵面团的面筋蛋白网络结构相对较为稳定。综上，大豆蛋白与面筋蛋白通过二硫键和非共价键相互作用，改变面筋蛋白网络结构的均匀性和连续性，从而引起馒头品质变化。In order to provide some theoretical basis for the application of soy protein in frozen fermented dough, the frozen fermented dough buns and frozen fermented doughs with soy protein isolates(SPI), texturised soy protein isolates(TSP), hydrolysed soy protein isolates (SPH)added were studied, the physicochemical indexes (specific volume, water content, pH) of frozen fermented dough buns added with different kinds of soy proteins were analysed, and the effect mechanism of different kinds of soy proteins on the quality of frozen fermented dough buns was investigated by determining the disulphide bond content, the secondary structure and microstructure of proteins in the frozen fermented doughs. The results showed that the specific volume of steamed buns before and after frozen storage of fermented dough with SPH had the smallest change, and the specific volume of steamed buns after 40 d of frozen storage increased by 4.59% compared with 10 d of frozen storage. The water content of steamed buns with SPH was the lowest under the same frozen storage time. The addition of soy protein could reduce the rate of pH drop of fermented dough buns during frozen storage, and could extend the shelf life of frozen fermented doughs by at least 10 d.The protein structure analysis of frozen fermented dough by Fourier infrared and laser confocal results showed that the addition of three soy proteins reduced the uniformity and continuity of gluten protein network structure.The effects of SPI and TSP on the gluten protein network structure were less than those of SPH, but the gluten protein network structure of fermented dough with SPH added during frozen storage was relatively stable.In summary, soy protein and gluten protein interact through S—S and non-covalent bonds to change the uniformity and continuity of the gluten protein network structure, causing changes in the quality of steamed buns.

Introduction: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a poor response to chemotherapy and an extremely poor prognosis. Recent studies have revealed that phospholysine phosphohistidine inorganic pyrophosphate phosphatase (LHPP) can inhibit the growth of various cancers. Therefore, the current study was conducted to investigate the antitumor effects of LHPP in PDAC and to explore its mechanism using proteomics analysis. Methods and Results: Immunohistochemical analysis of clinical samples demonstrated that LHPP expression levels were lower in tumor tissues compared to adjacent nontumor tissues. Moreover, multivariate COX regression analysis showed that LHPP expression level was an independent prognostic factor for the patients with PDAC. Patients with high LHPP expression had a better prognosis. The lentiviral vectors for normal control (NC), LHPP knockdown (KD), and LHPP overexpression (OE) were infected with BxPC-3 and PANC-1 cell lines. Cell counting kit-8 assay, Transwell assay, and flow cytometry analyses showed that LHPP overexpression significantly inhibited the cell viability, migration, and proliferation of BxPC-3 and PANC-1 cells. Moreover, xenograft tumor model demonstrated that LHPP overexpression inhibited xenograft tumor growth in vivo. Subsequently, proteins with significantly altered expression in BxPC-3 cells after lentivirus infection were detected using proteomics analyses. Interestingly, compared to the NC group, the expression of Syndecan 1 (SDC1) was significantly upregulated in the KD group, while that of S100P was significantly downregulated in the OE group. Conclusion: LHPP might emerge as an important target for delaying the advancement of PDAC, thereby providing a novel therapeutic approach for the treatment of PDAC. [ABSTRACT FROM AUTHOR]

Background: Cholangiocarcinoma (CHOL) is a malignant tumor that originates in the extrahepatic bile duct and can extend from the hilar region to the lower end of the common bile duct. The prognosis of CHOL patients is particularly poor; therefore, in this study, we screened mRNAs correlated with N6-methyladenosine (m6A) to construct a risk model for prognosis in CHOL.Methods: The TCGA-CHOL dataset was applied to obtain and analyze the coexpression of 1281 m6A-related mRNAs, from which 14 were selected for further analysis through univariate proportional hazards (cox) regression analysis. Aryl hydrocarbon receptor interacting protein (AIP), CCAAT/enhancer binding protein beta (CEBPB), syndecan1 (SDC1), vacuolar protein sorting 25 homolog (VPS25) and syntaxin binding protein 2 (STXBP2) were then screened out through the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to develop a precise m6A-related mRNA prognosis risk model (MRMRPM) with an area under curve (AUC) of 0.908 and 0.923 after 1 and 2 years, respectively. We divided the samples into high-risk and low-risk groups using the m6A-related mRNA prognosis risk model.Results: Kaplan-Meier analysis indicated poor overall survival (OS) for the high-risk group. Two Gene Expression Omnibus (GEO) datasets (GSE89748 and GSE107943) were used to validate the risk model. The results of drug sensitivity and immune cell infiltration analysis showed that the risk model could serve as a prognosis index of potential immunotherapeutic characteristics and drug sensitivity. Furthermore, the proportion of resting dendritic cells and regulatory T cells was positively associated with an increased expression of four m6A-related mRNAs - AIP, CEBPB, SDC1, and VPS25 - in the high-risk CHOL group.Conclusions: Our findings suggest that this model can be a prognostic indicator for CHOL patients. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) play a crucial role in various biological processes and human diseases, and are considered as therapeutic targets for small molecules (SMs). Due to the time-consuming and expensive biological experiments required to validate SM-miRNA associations, there is an urgent need to develop new computational models to predict novel SM-miRNA associations. The rapid development of end-to-end deep learning models and the introduction of ensemble learning ideas provide us with new solutions. Based on the idea of ensemble learning, we integrate graph neural networks (GNNs) and convolutional neural networks (CNNs) to propose a miRNA and small molecule association prediction model (GCNNMMA). Firstly, we use GNNs to effectively learn the molecular structure graph data of small molecule drugs, while using CNNs to learn the sequence data of miRNAs. Secondly, since the black-box effect of deep learning models makes them difficult to analyze and interpret, we introduce attention mechanisms to address this issue. Finally, the neural attention mechanism allows the CNNs model to learn the sequence data of miRNAs to determine the weight of sub-sequences in miRNAs, and then predict the association between miRNAs and small molecule drugs. To evaluate the effectiveness of GCNNMMA, we implement two different cross-validation (CV) methods based on two different datasets. Experimental results show that the cross-validation results of GCNNMMA on both datasets are better than those of other comparison models. In a case study, Fluorouracil was found to be associated with five different miRNAs in the top 10 predicted associations, and published experimental literature confirmed that Fluorouracil is a metabolic inhibitor used to treat liver cancer, breast cancer, and other tumors. Therefore, GCNNMMA is an effective tool for mining the relationship between small molecule drugs and miRNAs relevant to diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Niu, Gao, Xia, Zhao, Sun, Wang, Liu, Kong, Ma, Zhu, Gao, Liu, Yang, Song, Lu and Zhou.)