Network pharmacology-based prediction of the active ingredients and mechanism of Shen Gui capsule for application to coronary heart disease

Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review

Cardiovascular diseases remain the main cause of death worldwide which makes it essential to better understand, diagnose, and treat atherosclerosis. Artificial intelligence (AI) and novel technological solutions offer us new possibilities and enable the practice of individually tailored medicine. The study was performed using the PRISMA protocol. As of January 10, 2023, the analysis has been based on a review of 457 identified articles in PubMed and MEDLINE databases. The search covered reviews, original articles, meta-analyses, comments, and editorials published in the years 2009-2023. In total, 123 articles met inclusion criteria. The results were divided into the subsections presented in the review (genome-wide association studies, radiomics, and other studies). This paper presents actual knowledge concerning atherosclerosis, in silico, and big data analyses in cardiology that affect the way medicine is practiced in order to create an individual approach and adjust the therapy of atherosclerosis.

Uncovering the molecular mechanisms of Fructus Choerospondiatis against coronary heart disease using network pharmacology analysis and experimental pharmacology

Fructus Choerospondiatis (FC), a Mongolian medicine, was mainly used in Mongolian medical theory for the treatment of coronary heart disease (CHD). Nonetheless, the main components and mechanisms of action of FC in the treatment of coronary artery disease have not been studied clearly.

AIM OF THE STUDY

The aim of this study is to identify the components of FC and analyze the pathways affected by the targets of these components to probe into the potential mechanisms of action of FC on coronary heart disease.

MATERIALS AND METHODS

Identification of compounds in FC employing high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (HPLC-QTOF-MS) method, then further investigate the network pharmacology and molecular docking to obtain potential targets and elucidate the potential mechanism of action of FC in the therapy of CHD. Experimental validation was established to verify the mechanism of FC in vitro.

RESULTS

21 FC components were identified and 65 overlapping targets were gained. In addition, these ingredients regulated AMPK and PPAR signaling pathway by 65 target genes including IL6, AKT1 and PPARg, etc. Molecular docking displayed that the binding ability of the key target PPARg to FC components turned out to be better. Experimental validation proved that FC treatment decreased the expression of PPARg (p < 0.05) compare with model group, which may be involved in the PPAR signaling pathway.

CONCLUSIONS

This study was the first to elucidate the mechanism of action of components of FC for the treatment of CHD using network pharmacology. It alleviated CHD by inhibiting the expression of PPARg to attenuate hypoxia/reoxygenation injury, and the results give a basis for elucidating the molecular mechanism of action of FC for the treatment of coronary heart disease.

Therapeutic mechanism of Curcuma aromatica Salisb. rhizome against coronary heart disease based on integrated network pharmacology, pharmacological evaluation and lipidomics

Curcuma aromatica Salisb. rhizome (CASR) has multifunctional characteristics worldwide and a long history of use as a botanical drug with. Currently, it is often used clinically to treat coronary heart disease (CHD) caused by blood stasis syndrome. However, the therapeutic mechanism of CASR in the treatment of CHD remains poorly understood. In study, the main chemical constituents of CASR were analyzed using UPLC-Q-TOF-MS/MS. Then, its potential therapeutic mechanism against CHD was predicted. Subsequently, pharmacological evaluation was performed using CHD rat model. Finally, a lipidomics approach was applied to explore the different lipid metabolites to verify the regulation of CASR on lipid metabolism disorders in CHD. A total of 35 compounds was identified from CASR. Seventeen active components and 51 potential targets related to CHD were screened by network pharmacology, involving 13 key pathways. In vivo experiments showed that CASR could significantly improve myocardial infarction, blood stasis, and blood lipid levels and regulate the PI3K/AKT/mTOR signaling pathway in CHD rats. Lipidomics further showed that CASR could regulate abnormal sphingolipid, glycerophospholipid, and glycerolipid metabolism in CHD rats. The therapeutic mechanism of CASR against CHD was initially elucidated and included the regulation of lipid metabolism. Its effects may be attributed to active ingredients, such as curzerene, isoprocurcumenol, and (+)-curcumenol. This study reveals the characteristics of multi-component and multi-pathway of CASR in the treatment of CHD, which provides a basis for the follow-up development and utilization of CASR.

Generative Adversarial Networks in Medical Image augmentation: A review

OBJECT

With the development of deep learning, the number of training samples for medical image-based diagnosis and treatment models is increasing. Generative Adversarial Networks (GANs) have attracted attention in medical image processing due to their excellent image generation capabilities and have been widely used in data augmentation. In this paper, a comprehensive and systematic review and analysis of medical image augmentation work are carried out, and its research status and development prospects are reviewed.

METHOD

This paper reviews 105 medical image augmentation related papers, which mainly collected by ELSEVIER, IEEE Xplore, and Springer from 2018 to 2021. We counted these papers according to the parts of the organs corresponding to the images, and sorted out the medical image datasets that appeared in them, the loss function in model training, and the quantitative evaluation metrics of image augmentation. At the same time, we briefly introduce the literature collected in three journals and three conferences that have received attention in medical image processing.

RESULT

First, we summarize the advantages of various augmentation models, loss functions, and evaluation metrics. Researchers can use this information as a reference when designing augmentation tasks. Second, we explore the relationship between augmented models and the amount of the training set, and tease out the role that augmented models may play when the quality of the training set is limited. Third, the statistical number of papers shows that the development momentum of this research field remains strong. Furthermore, we discuss the existing limitations of this type of model and suggest possible research directions.

CONCLUSION

We discuss GAN-based medical image augmentation work in detail. This method effectively alleviates the challenge of limited training samples for medical image diagnosis and treatment models. It is hoped that this review will benefit researchers interested in this field.

Identification of the active compounds and their mechanisms of medicinal and edible Shanzha based on network pharmacology and molecular docking

Shanzha (Crataegus pinnatifida Bunge), an edible traditional Chinese medicine (TCM), has an effect on dyspepsia. However, the investigations of the pharmacological effects have not been carried out. This study aimed to identify the potential targets and pharmacological mechanisms of Shanzha in the treatment of dyspepsia by network pharmacology and molecular docking. Five active compounds and 13 key targets were obtained by a set of bioinformatics assays. Vitexin 7-glucoside, suchilactone, and 20-hexadecanoylingenol were the main compounds acting on dyspepsia. The key targets were prostaglandin-endoperoxide synthase 2 (PTGS2), serine/threonine-protein kinase mTOR (MTOR), heat shock protein HSP 90-alpha (HSP90AA1), mitogen-activated protein kinase 1 (MAPK1), MAPK3, E3 ubiquitin-protein ligase Mdm2 (MDM2), receptor tyrosine-protein kinase erbB-2 (ERBB2), caspase-3 (CASP3), matrix metalloproteinase-9 (MMP9), estrogen receptor (ESR1), tumor necrosis factor (TNF), phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA), and peroxisome proliferator-activated receptor gamma (PPARG), which played the vital roles in TNF, prostate cancer, thyroid hormone, hepatitis B and estrogen signaling pathway. The molecular mechanisms of Shanzha regulating dyspepsia were mainly related to reduction of inflammatory response, controlling cell proliferation and survival, increasing intestinal moisture, and promoting intestinal motility. PRACTICAL APPLICATIONS: Shanzha has been used as an edible TCM to improve digestion for a long time. However, the ingredients and mechanisms of Shanzha in the treatment of dyspepsia are not clear. In this research, network pharmacological analysis integrated with molecular docking was conducted to investigate the molecular mechanism. The results suggested that the core targets alleviated dyspepsia by reducing the intestinal inflammatory response, increasing intestinal movement, controlling cell physiological activities, and reducing constipation. In summary, this study demonstrated the multiple compounds, targets, and pathways characteristics of Shanzha in the treatment of dyspepsia, which may provide guidance and foundations for further application of edible medicine.

Screening druggable targets and predicting therapeutic drugs for COVID-19 via integrated bioinformatics analysis

BACKGROUND

Since the outbreak of coronavirus disease 2019 (COVID-19) in China, numerous research institutions have invested in the development of anti-COVID-19 vaccines and screening for efficacious drugs to manage the virus.

OBJECTIVE

To explore the potential targets and therapeutic drugs for the prevention and treatment of COVID-19 through data mining and bioinformatics.

METHODS

We integrated and profoundly analyzed 10 drugs previously assessed to have promising therapeutic potential in COVID-19 management, and have been recommended for clinical trials. To explore the mechanisms by which these drugs may be involved in the treatment of COVID-19, gene-drug interactions were identified using the DGIdb database after which functional enrichment analysis, protein-protein interaction (PPI) network, and miRNA-gene network construction were performed. We adopted the DGIdb database to explore the candidate drugs for COVID-19.

RESULTS

A total of 43 genes associated with the 10 potential COVID-19 drugs were identified. Function enrichment analysis revealed that these genes were mainly enriched in response to other invasions, toll-like receptor pathways, and they play positive roles in the production of cytokines such as IL-6, IL-8, and INF-β. TNF, TLR3, TLR7, TLR9, and CXCL10 were identified as crucial genes in COVID-19. Through the DGIdb database, we predicted 87 molecules as promising druggable molecules for managing COVID-19.

CONCLUSIONS

Findings from this work may provide new insights into COVID-19 mechanisms and treatments. Further, the already identified candidate drugs may improve the efficiency of pharmaceutical treatment in this rapidly evolving global situation.

Exploring the Mechanism of Gyejibokryeong-hwan against Atherosclerosis Using Network Pharmacology and Molecular Docking

Gyejibokryeong-hwan (GBH) is a traditional formula comprised of five herbal medicines that is frequently used to treat blood stasis and related complex multifactorial disorders such as atherosclerosis. The present study used network pharmacology and molecular docking simulations to clarify the effect and mechanism of the components of GBH. Active compounds were selected using Oriental Medicine Advanced Searching Integrated System (OASIS) and the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), and target genes linked to the selected components were retrieved using Search Tool for Interacting Chemicals (STITCH) and GeneCards. Functional analysis of potential target genes was performed through the Annotation, Visualization and Integrated Discovery (DAVID) database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and molecular docking confirmed the correlation between five core compounds (quercetin, kaempferol, baicalein, ellagic acid, and baicalin) and six potential target genes (AKT1, CASP3, MAPK1, MAPK3, NOS2, and PTGS2). Molecular docking studies indicated that quercetin strongly interacted with six potential target proteins. Thus, these potential target proteins were closely related to TNF, HIF-1, FoxO, and PI3K-Akt signal pathways, suggesting that these factors and pathways may mediate the beneficial effects of GBH on atherosclerosis. Our results identify target genes and pathways that may mediate the clinical effects of the compounds contained within GBH on atherosclerosis.