Higher education blended learning: a network study of student learning paths

For the aim of this research, a model of students' learning pathways as a network that records time and sequence of learning events is introduced. These learning pathways networks also establish the online learning rate. Results demonstrate that pupils who passed learned more online than those who failed. The findings of the person-centered study demonstrate that only test anxiety affects the number of nodes and arcs of the individual learning pathway network, which was represented as an individual network. Also, the qualities of social networks are influenced by how active or inactive users are. There is evidence to back up the theory that pupils who are driven to learn do not necessarily study more information. As a result of exam anxiety, engagement, and disengagement, the learning pathways of individuals are shaped and reshaped throughout time.

Network pharmacology approach to reveals therapeutic mech-anism of traditional plants formulation used by Malaysia in-digenous ethnics in coronaviruses infection

Network pharmacology analysis can act as a strategy to identify the pharmacological effect of plant-based bioactive compounds against coronavirus diseases. This study aimed to investigate the potential pharmacological mechanism of a local ethnomedicine (Costus speciosus, Hibiscus rosa-sinensis and Phyllanthus niruri) of Northern Borneo against coronaviruses known as CHP. Compounds in CHP were extracted from databases and screened for their oral bioavailability and drug-likeness before a compound-target network was built. Furthermore, the protein-protein interaction network and pathway enrichment were constructed and analyzed. A compound-target network consisting of 48 putative bioactive compounds targeting 587 candidate genes was identified. A total of 186 coronavirus-related genes were extracted and TP53, STAT3, HSP90AA1, STAT1, and EP300 were predicted to be the key targets. Notably, mapping of these target genes into the target-pathway network illustrated that functional enrichment was on viral infection and regulation of inflammation pathways. Urinatetralin is predicted, for the first time, as a bioactive compound that solely targets STAT3. The results from this study indicate that compounds present in CHP employ STAT3 and its connected pathways as the mechanism of action against coronaviruses. In conclusion, urinatetralin should be further investigated for its potential application against coronavirus infections.

Investigating the Mechanisms of Pollen Typhae in the Treatment of Diabetic Retinopathy Based on Network Pharmacology and Molecular Docking

Objective. To explore the main bioactive compounds and investigate the underlying mechanism of Pollen Typhae (PT) against diabetic retinopathy (DR) by network pharmacology and molecular docking analysis. Methods. Bioactive ingredients and the target proteins of PT were obtained from TCMSP, and the related target genes were acquired from the SwissTargetPrediction database. The target genes of DR were obtained from GeneCards, TTD database, DisGeNET database, and DrugBank. The compound-target interaction network was established based on Cytoscape 3.7.2. The protein-protein interaction (PPI) network was constructed via STRING database and Cytoscape 3.7.2. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were visualized through DAVID database and Bioinformatics. Ingredient-gene-pathway network analysis was conducted to further screen the ingredients, target proteins, and pathways closely related to the biological mechanism on PT for DR, and molecular docking analysis was performed by SYBYL-X 2.1.1 software. Finally, the mechanism and underlying targets of PT in the treatment of DR were predicted. Results. A total of 8 compounds and 171 intersection targets were obtained based on the online network database. 7 main compounds were screened from compound-target network, and 53 targets including the top six key targets (PTGS2, AKT1, VEGFA, MAPK3, TNF, and EGFR) were further acquired from PPI analysis. The 53 key targets covered 80 signaling pathways, among which PI3K-Akt signaling pathway, focal adhesion, Rap1 signaling pathway, VEGF signaling pathway, and HIF-1 signaling pathway were closely connected with the biological mechanism involved in the alleviation of DR by PT. Ingredient-gene-pathway network shows that AKTI, EGFR, and VEGFA were core genes, kaempferol and isorhamnetin were pivotal ingredients, and VEGF signaling pathway and Rap1 signaling pathway were closely involved in anti-DR. The docking results indicated that five main compounds (arachidonic acid, isorhamnetin, quercetin, kaempferol, and (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one) had good binding activity with EGFR and AKT1 targets. Conclusion. The active ingredients in PT may regulate the levels of inflammatory factors, suppress the oxidative stress, and inhibit the proliferation, migration, and invasion of retinal pericytes by acting on PTGS2, AKT1, VEGFA, MAPK3, TNF, and EGFR targets through VEGF signaling pathway, PI3K-Akt signaling pathway, Rap1 signaling pathway, and HIF-1 signaling pathway to play a therapeutic role in diabetic retinopathy.

Multiplex-Heterogeneous Network-Based Capturing Potential SNP “Switches” of Pathways Associating With Diverse Disease Characteristics of Asthma

Asthma is a complex heterogeneous respiratory disorder. In recent years nubbly regions of the role of genetic variants and transcriptome including mRNAs, microRNAs, and long non-coding RNAs in the pathogenesis of asthma have been separately excavated and reported. However, how to systematically integrate and decode this scattered information remains unclear. Further exploration would improve understanding of the internal communication of asthma. To excavate new insights into the pathogenesis of asthma, we ascertained three asthma characteristics according to reviews, airway inflammation, airway hyperresponsiveness, and airway remodeling. We manually created a contemporary catalog of corresponding risk transcriptome, including mRNAs, miRNAs, and lncRNAs. MIMP is a multiplex-heterogeneous networks-based approach, measuring the relevance of disease characteristics to the pathway by examining the similarity between the determined vectors of risk transcriptome and pathways in the same low-dimensional vector space. It was developed to enable a more concentrated and in-depth exploration of potential pathways. We integrated experimentally validated competing endogenous RNA regulatory information and the SNPs with significant pathways into the ceRNA-mediated SNP switching pathway network (CSSPN) to analyze ceRNA regulation of pathways and the role of SNP in these dysfunctions. We discovered 11 crucial ceRNA regulations concerning asthma disease feature pathway and propose a potential mechanism of ceRNA regulatory SNP → gene → pathway → disease feature effecting asthma pathogenesis, especially for MALAT1 (rs765499057/rs764699354/rs189435941) → hsa-miR-155 → IL13 (rs201185816/rs1000978586/rs202101165) → Interleukin-4 and Interleukin-13 signaling → inflammation/airway remodeling and MALAT1 (rs765499057/rs764699354/rs189435941) → hsa-miR-155 → IL17RB (rs948046241) → Interleukin-17 signaling (airway remodeling)/Cytokine-cytokine receptor interaction (inflammation). This study showed a systematic and propagable workflow for capturing the potential SNP “switch” of asthma through text and database mining and provides further information on the pathogenesis of asthma.

Potential Mechanisms of Triptolide against Diabetic Cardiomyopathy Based on Network Pharmacology Analysis and Molecular Docking

The incidence of heart failure was significantly increased in patients with diabetic cardiomyopathy (DCM). The therapeutic effect of triptolide on DCM has been reported, but the underlying mechanisms remain to be elucidated. This study is aimed at investigating the potential targets of triptolide as a therapeutic strategy for DCM using a network pharmacology approach. Triptolide and its targets were identified by the Traditional Chinese Medicine Systems Pharmacology database. DCM-associated protein targets were identified using the comparative toxicogenomics database and the GeneCards database. The networks of triptolide-target genes and DCM-associated target genes were created by Cytoscape. The common targets and enriched pathways were identified by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The gene-gene interaction network was analyzed by the GeneMANIA database. The drug-target-pathway network was constructed by Cytoscape. Six candidate protein targets were identified in both triptolide target network and DCM-associated network: STAT3, VEGFA, FOS, TNF, TP53, and TGFB1. The gene-gene interaction based on the targets of triptolide in DCM revealed the interaction of these targets. Additionally, five key targets that were linked to more than three genes were determined as crucial genes. The GO analysis identified 10 biological processes, 2 cellular components, and 10 molecular functions. The KEGG analysis identified 10 signaling pathways. The docking analysis showed that triptolide fits in the binding pockets of all six candidate targets. In conclusion, the present study explored the potential targets and signaling pathways of triptolide as a treatment for DCM. These results illustrate the mechanism of action of triptolide as an anti-DCM agent and contribute to a better understanding of triptolide as a transcriptional regulator of cytokine mRNA expression.

Predicting the Molecular Mechanism of Sini Jia Renshen Decoction in Treating Severe COVID-19 Patients Based on Network Pharmacology and Molecular Docking

Objective: To explore the potential mechanism of Sini jia Renshen Decoction (SJRD) in the treatment of COVID-19 based on network pharmacology and molecular docking. Methods: The active compounds and potential therapeutic targets of SJRD were collected through the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). Then a string database was used to build a protein–protein interactions (PPI) network between proteins, and use the David database to perform gene ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct an active ingredients-core target-signaling pathway network, and finally the active ingredients of SJRD were molecularly docked with the core targets to predict the mechanism of SJRD in the treatment of COVID-19. Results: A total of 136 active compounds, 51 core targets and 93 signaling pathways were selected. Molecular docking results revealed that quercetin, 3,22-dihydroxy-11-oxo-delta(12)-oleanene-27-alpha-methoxycarbonyl-29-oic acid, 18α-hydroxyglycyrrhetic acid, gomisin B and ignavine had considerable binding ability with ADRB2, PRKACA, DPP4, PIK3CG and IL6. Conclusions: This study preliminarily explored the mechanism of multiple components，multiple targets，and multiple pathways of SJRD in the treatment of COVID-19 by network pharmacology．

Identification and Characterization of MRNAs, lncRNAs, and MiRNAs Expression Profiles and ceRNA Networks in PEDV Infection

Background: Long non-coding RNAs (LncRNAs) are transcripts longer than 200 nucleotides with no protein-coding ability and exert crucial effects on viral infection and host immune responses. Porcine Epidemic Diarrhea Virus (PEDV) is a coronavirus that seriously affects the swine industry. However, our understanding of the function of lncRNA involved in host-PEDV interaction is limited.Results: A total of 1197 mRNA transcripts, 539 lncRNA transcripts, and 208 miRNA transcripts were differentially regulated at 24 h and 48 h post-infection. Moreover, gene ontology (GO) and KEGG pathway enrichment analysis showed that DE mRNAs and DE lncRNAs were mainly involved in biosynthesis, innate immunity, and lipid metabolism. Ten differentially expressed genes were randomly selected and validated by reverse-transcription qRT-PCR. In addition, we constructed a miRNA-mRNA-pathway network followed by a lncRNA-miRNA-mRNA ceRNA network.Conclusions: The present study is the first to reveal the global expression profiles of mRNAs, lncRNAs, and miRNAs during PEDV infection. We comprehensively characterize the ceRNA networks which can provide new insights into the pathogenesis of PEDV.