Extraction of Oleic Acid from Animals Oil and Its Anti-inflammatory Effect on Network Pharmacology

The purpose of this paper is to explore the possible mechanisms of anti-inflammatory and scar repair by Mongolian horse oil. We used TCM database and literature mining to collect active compounds of horse oil and used Swiss TargetPrediction and SuperPred server to find targets of compounds. Anti-inflammatory drug targets were collected through the CTD database. Go annotation of targets and KEGG pathway were enriched and analyzed through Metascape database platform. Molecular docking between active ingredients and targets was verified by AutoDock software. Metascape analysis revealed that the key candidate targets were significantly enriched in a number of pathways associated with inflammatory pathology. The results of molecular docking showed that oleic acid, a major component of animals oil, could influence the regulatory functions of TNF, NGF, IL6, IL1B, Jun, and CDK1. This suggests that animals oil can regulate the development of inflammation through its active ingredient, oleic acid, and can influence the expression of multiple signaling pathways, with theoretical endogenous interactions with TNF, NGF, IL6, IL1B, JUN, and CDK1 proteins.

Can online social interaction improve the digital finance participation of rural households?

PurposeDigital finance is a promising way to realize inclusive finance. However, the determinants of digital finance participation are largely unknown. This study aims to analyze the interface between social interaction and the digital finance participation of rural households and explore potential channels of social interaction to help them access digital finance.Design/methodology/approachUsing rural household survey data from China in 2017, employing the probit, ordered probit and count model, this study assesses the relationship between social interaction and digital finance.FindingsThe authors find that active online social interaction of rural households promotes digital finance participation, which also increases the depth and breadth of digital finance usage. Meanwhile, the role of traditional offline social interaction is insignificant. Contextual interaction is the channel through which online social interaction influences digital finance participation. Moreover, word-of-mouth, common topic pleasure and social norms in endogenous interactions are irrelevant. In addition, the role of online social interaction complements offline social interaction at promoting digital finance participation.Originality/valueThis study contributes to the understanding of digital finance by investigating the possible channels by which social interaction influences digital finance participation and highlight an important channel–contextual interaction, especially for online social interaction. This study expands the content of social interaction from traditional offline social interaction to online social interaction to evaluate the interface between social interaction and financial behavior more comprehensively.

Preliminary Study on the Mechanism of Carvacrol Regulating Hepatocellular Carcinoma Based on Network Pharmacology

Objective: To predict and analyze the target of anti-Hepatocellular Carcinoma (HCC) in the active constituents of Safflower by using network pharmacology. Methods: The active compounds of safflower were collected by TCMSP, TCM-PTD database and literature mining methods. The targets of active compounds were predicted by Swiss Target Prediction server, and the target of anti-HCC drugs was collected by DisGeNET database. The target was subjected to an alignment analysis to screen out Carvacrol, a target of safflower against HCC. The mouse HCC model was established and treated with Carvacrol. The anti-HCC target DAPK1 and PPP2R2A were verified by Western blot and co-immunoprecipitation. Results: A total of 21 safflower active ingredients were predicted. Carvacrol was identified as a possible active ingredient according to the five principles of drug-like medicine. According to Carvacrol's possible targets and possible targets of HCC, three co-targets were identified, including cancer- related are DAPK1 and PPP2R2A. After 20 weeks of Carvacrol treated, Carvacrol group significantly increased on DAPK1 levels and decreased PPP2R2A levels in the model mice by Western blot. Immunoprecipitation confirmed the endogenous interaction between DAPK1 and PPP2R2A. Conclusion: Safflower can regulate the development of HCC through its active component Carvacrol, which can affect the expression of DAPK1 and PPP2R2A proteins, and the endogenous interactions of DAPK1 and PPP2R2A proteins.

Data Science Issues in Studying Protein–RNA Interactions with CLIP Technologies

An interplay of experimental and computational methods is required to achieve a comprehensive understanding of protein–RNA interactions. UV crosslinking and immunoprecipitation (CLIP) identifies endogenous interactions by sequencing RNA fragments that copurify with a selected RNA-binding protein under stringent conditions. Here we focus on approaches for the analysis of the resulting data and appraise the methods for peak calling, visualization, analysis, and computational modeling of protein–RNA binding sites. We advocate that the sensitivity and specificity of data be assessed in combination for computational quality control. Moreover, we demonstrate the value of analyzing sequence motif enrichment in peaks assigned from CLIP data and of visualizing RNA maps, which examine the positional distribution of peaks around regulated landmarks in transcripts. We use these to assess how variations in CLIP data quality and in different peak calling methods affect the insights into regulatory mechanisms. We conclude by discussing future opportunities for the computational analysis of protein–RNA interaction experiments.

Data Science Issues in Understanding Protein-RNA Interactions

An interplay of experimental and computational methods is required to achieve a comprehensive understanding of protein-RNA interactions. Crosslinking and immunoprecipitation (CLIP) identifies endogenous interactions by sequencing RNA fragments that co-purify with a selected RBP under stringent conditions. Here we focus on approaches for the analysis of resulting data and appraise the methods for peak calling, visualisation, analysis and computational modelling of protein-RNA binding sites. We advocate a combined assessment of cDNA complexity and specificity for data quality control. Moreover, we demonstrate the value of analysing sequence motif enrichment in peaks assigned from CLIP data, and of visualising RNA maps, which examine the positional distribution of peaks around regulated landmarks in transcripts. We use these to assess how variations in CLIP data quality, and in different peak calling methods, affect the insights into regulatory mechanisms. We conclude by discussing future opportunities for the computational analysis of protein-RNA interaction experiments.