Introduction: Shuanghuanglian (SHL) oral liquid is a well-known traditional Chinese medicine preparation administered for respiratory tract infections in China. However, the underlying pharmacological mechanisms remain unclear. The present study aims to determine the potential pharmacological mechanisms of SHL oral liquid based on network pharmacology. Methods: A network pharmacology-based strategy including collection and analysis of putative compounds and target genes, network construction, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Ontology (GO) enrichment, identification of key compounds and target genes, and molecule docking was performed in this study. Results: A total of 82 bioactive compounds and 226 putative target genes of SHL oral liquid were collected. Of note, 28 hub target genes including 4 major hub target genes: estrogen receptor 1 (ESR1), nuclear receptor coactivator 2 (NCOA2), nuclear receptor coactivator 1 (NCOA1), androgen receptor (AR) and 5 key compounds (quercetin, luteolin, baicalein, kaempferol and wogonin) were identified based on network analysis. The hub target genes mainly enriched in pathways including PI3K-Akt signaling pathway, human cytomegalovirus infection, and human papillomavirus infection, which could be the underlying pharmacological mechanisms of SHL oral liquid for treating diseases. Moreover, the key compounds had great molecule docking binding affinity with the major hub target genes. Conclusion: Using network pharmacology analysis, SHL oral liquid was found to contain anti-virus, anti-inflammatory, and "multi-compounds and multi-targets" with therapeutic actions. These findings may provide a valuable direction for further clinical application and research. 1. Background SHL oral liquid is a well-known traditional Chinese medicine preparation frequently applied to treat acute upper respiratory tract infection, acute bronchitis, and pneumonia [1]. SHL oral liquid is derived from three Chinese medicinal herbs, namely, Flos Lonicerae (Jinyinhua, JYH), Radix Scutellariae (Huangqin, HQ), and Fructus Forsythiae (Lianqiao, LQ), which were officially recorded in the Chinese
Introduction: Lumbar disc herniation (LDH) is the most common cause of low back pain and severely affects people's quality of life and ability to work. Although many clinical trials and medical reports conducted over the years have shown that acupuncture treatments are effective for LDH, the comparative effectiveness of these different acupuncture therapies is still unclear. This protocol of a network meta-analysis was designed to compare the effects and safety of acupuncture treatment regimens on LDH using both direct and indirect evidence. Methods and analysis: This protocol is reported according to the 2015 PRISMA-P and PRISMA guidelines for acupuncture. Eight databases and two platforms will be searched for articles published from their establishment to 1 December 2020 with medical subject heading terms and keywords. Three reviewers will verify the eligible randomized controlled trials independently. NoteExpress (3.2.0) software will be utilized to manage the literature. The overall quality of evidence will be evaluated by Confidence In Network Meta-Analysis (CINeMA). Additionally, we will conduct a meta-analysis of the effectiveness, recurrence rate, and symptom score of acupuncture in treating LDH using Review Manager (RevManV.5.4.1) and R4.0.2 software (The R Foundation for Statistical Computing). Results: The results of the study will be published in journals or relevant conferences. Conclusion: This proposed systematic review will evaluate the comparative efficacy and safety of various acupuncture methods and combination protocols for LDH.
Objective: Shufeng Jiedu capsule (SFJDC) is a well-known Chinese patent drug that is recommended as a basic prescription and applied widely in the clinical treatment of COVID-19. However, the exact molecular mechanism of SFJDC remains unclear. The present study aims to determine the potential pharmacological mechanisms of SFJDC in the treatment of COVID-19 based on network pharmacology. Methods: The network pharmacology-based strategy includes collection and analysis of active compounds and target genes, network construction, identification of key compounds and hub target genes, KEGG and GO enrichment, recognition and analysis of main modules, as well as molecule docking. Results: A total of 214 active chemical compounds and 339 target genes of SFJDC were collected. Of note, 5 key compounds ( β -sitosterol, luteolin, kaempferol, quercetin, and stigmasterol) and 10 hub target genes (TP53, AKT1, NCOA1, EGFR, PRKCA, ANXA1, CTNNB1, NCOA2, RELA and FOS) were identified based on network analysis. The hub target genes mainly enriched in pathways including MAPK signaling pathway, PI3K-Akt signaling pathway and cAMP signaling pathway, which could be the underlying pharmacological mechanisms of SFJDC for treating COVID-19. Moreover, the key compounds had high binding activity with three typical target genes. Conclusions: y network pharmacology analysis, SFJDC was found to effectively improve immune function and reduce inflammatory responses based on its key compounds, hub target genes, and the relevant pathways. These findings may provide valuable evidence for explaining how SFJDC exerting the therapeutic effects on COVID-19, providing a holistic view for further clinical application.
To enhance the depth of excavation and promote the intelligence of acupoint compatibility, a method of constructing weighted network, which combines the attributes of acupoints and supervised learning, is proposed for link prediction. Medical cases of cervical spondylosis with acupuncture treatment are standardized, and a weighted network is constructed according to acupoint attributes. Multiple similarity features are extracted from the network and input into a supervised learning model for prediction. And, the performance of the algorithm is evaluated through evaluation indicators. The experiment finally screened 67 eligible medical cases, and the network model involved 141 acupoint nodes with 1048 edge. Except for the Preferential Attachment similarity index and the Decision Tree model, all other similarity indexes performed well in the model, among which the combination of PI index and Multilayer Perception model had the best prediction effect with an AUC value of 0.9351, confirming the feasibility of weighted networks combined with supervised learning for link prediction, also as a strong support for clinical point selection.
Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the leading cause of coronavirus disease-2019 (COVID-19), is an emerging global health crisis. Lung cancer patients are at a higher risk of COVID-19 infection. With the increasing number of non-small-cell lung cancer (NSCLC) patients with COVID-19, there is an urgent need of efficacious drugs for the treatment of COVID-19/NSCLC.Methods: Based on a comprehensive bioinformatic and systemic biological analysis, this study investigated COVID-19/NSCLC interactional hub genes, detected common pathways and molecular biomarkers, and predicted potential agents for COVID-19 and NSCLC.Results: A total of 122 COVID-19/NSCLC interactional genes and 21 interactional hub genes were identified. The enrichment analysis indicated that COVID-19 and NSCLC shared common signaling pathways, including cell cycle, viral carcinogenesis, and p53 signaling pathway. In total, 10 important transcription factors (TFs) and 44 microRNAs (miRNAs) participated in regulations of 21 interactional hub genes. In addition, 23 potential candidates were predicted for the treatment of COVID-19 and NSCLC.Conclusion: This study increased our understanding of pathophysiology and screened potential drugs for COVID-19 and NSCLC.
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