Traditional Database Research Articles

Mechanism and compatibility efficacy of Astragali Radix-Chuanxiong Rhizoma drug pair in treating ischemic stroke based on network regulation

Based on the association network of "drug pair-disease", the effect characteristics of Astragali Radix-Chuanxiong Rhizoma drug pair in the treatment of ischemic stroke(IS) with Qi deficiency and blood stasis and the matching mechanism of the two were explored. Through Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP) and SwissTargetPrediction Database, the effective chemical components of the drug pair were screened, and the candidate targets were predicted. Databa-ses such as GeneCards, DrugBank, Online Mendelian Inheritance in Man(OMIM), and Therapeutic Target Database(TTD) were searched to obtain gene targets related to IS. Through STRING and Cytoscape 3.9.1 software, the protein-protein interaction(PPI) network was constructed by using the interaction information of disease syndrome-related genes and candidate targets of drug pairs, and the core targets were screened according to the network topological feature values. Based on the Metascape platform and DAVID database, the biomolecular interaction information was integrated to analyze the Kyoto Encyclopedia of Genes and Genomes(KEGG) and mine biological functions, so as to further explore the mechanism of action and compatibility characteristics of Astragali Radix-Chuan-xiong Rhizoma. The results showed that the candidate biological process was mainly involved in the regulation of functional modules such as immune, blood circulation, neurotransmitter, and oxidative stress, and it was enriched in lipid and atherosclerosis, calcium signaling pathway, and platelet activation. Astragali Radix and Chuanxiong Rhizoma have their own characteristics. Astragali Radix has a regulatory response to growth factors while maintaining the body's immune balance, while Chuanxiong Rhizoma mainly improves the circulatory system and participates in hormone metabolism, so as to indicate the compatibility mechanism of Astragali Radix-Chuanxiong Rhizoma drug pair for multi-target and multi-pathway synergistic treatment of IS. Through further experimental verification, it was found that the Astragali Radix-Chuanxiong Rhizoma drug pair could significantly down-regulate the expression of key targets including TLR4, NF-κB, IL-1β, F2R, PLCβ1, and MYLK. This study preliminarily reveals that the Astragali Radix-Chuanxiong Rhizoma drug pair may play the three replenishing effects of promoting blood circulation, benefiting Qi, and clearing collaterals by correcting immune di-sorders, blood circulation disorders, and inflammation, which provide support for the clinical research on the subsequent improvement of Qi deficiency and blood stasis in the treatment of IS and provide a new idea for the analysis of modern biological connotation of the compatibility of seven emotions of traditional Chinese medicine.

Yantiao Formula Intervention in Rats with Sepsis: Network Pharmacology and Experimental Analysis.

Traditional Chinese Medicine prescribes Yantiao Formula (YTF; peach kernel, mirabilite, Angelica sinensis, Radix Scrophulariae, raw rhubarb, Radix Paeoniae, Flos Lonicerae, Forsythia and Ophiopogon japonicus) to treat sepsis. Clinically, it reduced the inflammatory response of sepsis. It also reduced lung damage by decreasing the level of TNF-α in septic rats' serum. Using network pharmacology analysis, we investigated the efficacy network and mechanism of YTF in treating sepsis. We used the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and a Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine (BATMAN-TCM) combined with literature to collect the main components in YTF and their targets. DisGeNET and GENECARDS databases were used for sepsis-related targets. Cytoscape 3.7.1 software was used to construct the herbcomponent- target and ingredient-target-disease interaction protein-protein interaction networks of YTF. The jvenn was used to perform the intersection of YTF targets and sepsis targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed． We also created a sepsis rat model using cecal ligation and perforation and stimulated alveolar macrophages (NR8383) with endotoxin to investigate the mechanisms of YTF. GO, and KEGG enrichment analysis revealed that these targets involved mineralocorticoid secretion, aldosterone secretion, active regulation of chronic inflammatory response, the exogenous coagulation pathway, and other pathophysiology. It was linked to various inflammatory factors and the MAPK pathway. YTF inhibits the p38MAPK pathway and decreases TNF- α, IL-6, and CXCL8 levels. YTF has a multi-component, multi-target, and multi-channel role in treating sepsis. The primary mechanisms may involve inhibiting the p38MAPK pathway to reduce the inflammatory response.

Utilizing network pharmacology, molecular docking, and animal models to explore the therapeutic potential of the WenYang FuYuan recipe for cerebral ischemia-reperfusion injury through AGE-RAGE and NF-κB/p38MAPK signaling pathway modulation

BackgroundStroke is a debilitating condition with high morbidity, disability, and mortality that significantly affects the quality of life of patients. In China, the WenYang FuYuan recipe is widely used to treat ischemic stroke. However, the underlying mechanism remains unknown, so exploring the potential mechanism of action of this formula is of great practical significance for stroke treatment. ObjectiveThis study employed network pharmacology, molecular docking, and in vivo experiments to clarify the active ingredients, potential targets, and molecular mechanisms of the WenYang FuYuan recipe in cerebral ischemia-reperfusion injury, with a view to providing a solid scientific foundation for the subsequent study of this recipe. Materials and methodsActive ingredients of the WenYang FuYuan recipe were screened using the traditional Chinese medicine systems pharmacology database and analysis platform. Network pharmacology approaches were used to explore the potential targets and mechanisms of action of the WenYang FuYuan recipe for the treatment of cerebral ischemia-reperfusion injury. The Middle Cerebral Artery Occlusion/Reperfusion 2 h Sprague Dawley rat model was prepared, and TTC staining and modified neurological severity score were applied to examine the neurological deficits in rats. HE staining and Nissl staining were applied to examine the pathological changes in rats. Immunofluorescence labeling and Elisa assay were applied to examine the expression levels of certain proteins and associated factors, while qRT-PCR and Western blotting were applied to examine the expression levels of linked proteins and mRNAs in disease-related signaling pathways. ResultsWe identified 62 key active ingredients in the WenYang FuYuan recipe, with 222 highly significant I/R targets, forming 138 pairs of medication components and component-targets, with the top five being Quercetin, Kaempferol, Luteolin, β-sitosterol, and Stigmasterol. The key targets included TP53, RELA, TNF, STAT1, and MAPK14 (p38MAPK). Targets related to cerebral ischemia-reperfusion injury were enriched in chemical responses, enzyme binding, endomembrane system, while enriched pathways included lipid and atherosclerosis, fluid shear stress and atherosclerosis, AGE-RAGE signaling in diabetic complications. In addition, the main five active ingredients and targets in the WenYang FuYuan recipe showed high binding affinity (e.g. Stigmasterol and MAPK14, total energy <−10.5 Kcal/mol). In animal experiments, the WenYang FuYuan recipe reduced brain tissue damage, increased the number of surviving neurons, and down-regulated S100β and RAGE protein expression. Moreover, the relative expression levels of key targets such as TP53, RELA and p38MAPK mRNA were significantly down-regulated in the WenYang FuYuan recipe group, and serum IL-6 and TNF-a factor levels were reduced. After WenYang FuYuan recipe treatment, the AGE-RAGE signaling pathway and downstream NF-kB/p38MAPK signaling pathway-related proteins were significantly modulated. ConclusionThis study utilized network pharmacology, molecular docking, and animal experiments to identify the potential mechanism of the WenYang FuYuan recipe, which may be associated with the regulation of the AGE-RAGE signaling pathway and the inhibition of target proteins and mRNAs in the downstream NF-kB/p38MAPK pathway.

From Text to Recommendations: How Vector Databases are Revolutionizing Personalized Content Delivery

Abstract: Effective similarity search and retrieval are now possible thanks to vector databases, which have become a potent tool for organizing and searching high-dimensional data. This article provides a thorough examination of vector databases, their underlying theories, and their applications across a range of industries. In handling complex data types, we address the significance of vector representation and emphasize the benefits of vector databases over traditional databases [1]. The article explores the process of creating a vector database, highlighting the critical function of indexing strategies such as IVF (Inverted File Indexing) and HNSW (Hierarchical Navigable Small World) in guaranteeing the effectiveness and precision of searches [2]. In addition, we discuss the problems caused by the curse of dimensionality and offer solutions to lessen its effects on nearest neighbor searches [3]

A Performance Analysis of CassandraDB and ScyllaDB

Abstract: Big Data encompasses vast amounts of data, reaching exabytes or even zettabytes. In the current landscape, large databases play a crucial role, particularly in generating substantial data for daily analyses of social media and multimedia content. The enormity of Big Data poses challenges, given its extensive volume of structured, semi-structured, and unstructured data, making traditional database systems and software techniques insufficient. Big Data is frequently defined by its 9 V’s: velocity, variety, volume, veracity, validity, variability, volatility, visualization, and value. This complexity highlights the need for a simple information management strategy that integrates various new data types alongside traditional data. The significance of Big Data databases is emphasized by the daily generation of millions of terabytes of data from sources like social media posts and multimedia. This study aims to evaluate the performance of two Wide-Column Store Big Data database systems, Apache CassandraDB and ScyllaDB, using the CassandraStress Benchmarking Tool. Key metrics such as total operation time, operation rate, partition rate, row rate, and maximum latency will be assessed as the number of records and operations increase. The development of these databases, motivated by diverse industry requirements, emphasizes their adaptation to specific needs. The research methodology outlines the tool used to compare these WideColumn Store databases on various parameters, contributing valuable insights into their performance in real-world scenarios

Reinvestigating the Correctness of Decoy-Based False Discovery Rate Control in Proteomics Tandem Mass Spectrometry.

Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, "open modification" database search strategies, in which the requirement that the mass of the database peptide closely matches the observed precursor mass is relaxed, have become popular as ways to find a wider variety of types of PTMs. Indeed, in one study, Kong et al. reported that the open modification search tool MSFragger can achieve higher statistical power to detect peptides than a traditional "narrow window" database search. We investigated this claim empirically and, in the process, uncovered a potential general problem with false discovery rate (FDR) control in the machine learning postprocessors Percolator and PeptideProphet. This problem might have contributed to Kong et al.'s report that their empirical results suggest that false discovery (FDR) control in the narrow window setting might generally be compromised. Indeed, reanalyzing the same data while using a more standard form of target-decoy competition-based FDR control, we found that, after accounting for chimeric spectra as well as for the inherent difference in the number of candidates in open and narrow searches, the data does not provide sufficient evidence that FDR control in proteomics MS/MS database search is inherently problematic.

Registering Ternate City People’s Traditional Knowledge as Property Rights

&lt;p&gt;Traditional knowledge is a subset of intellectual activity that stems from a country's collective ideas, opinions, or discoveries. This research aims to analyze: (1) The municipal government provides legal protection for intellectual property rights in traditional knowledge to increase the welfare of Ternate City inhabitants and (2) the potential for registering intellectual property rights related to traditional knowledge from 2019 to 2023 in Ternate City. The legislation method and the case approach were used in this empirical study. The most feasible way for the Indonesian government to protect traditional knowledge now is to strengthen the database on traditional knowledge. Special regulations are urgently needed that regulate the implementation of licensing procedures for users of Traditional Knowledge. The urgency of this research because It was found that from 2019 to 2023 in Ternate City, no traditional knowledge database yet existed. On this basis, there needs to be tactical steps from the Ternate City Regional Government in encouraging city branding about the utilization of traditional knowledge. The novelty of this research because Ternate has an abundant amount of traditional knowledge from its hundreds of ethnic groups that need to be legally protected but nearly zero traditional knowledge is processed with property rights. This paper is urgent and it contributes to science as it provides knowledge on how to legally protect this valuable knowledge.&lt;/p&gt;&lt;p&gt;&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt; &lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;

Analysis and Implementation of a Simple Database Management Software Based on Python and Excel

This paper aims to address the challenges faced by small and medium-sized enterprises (SMEs) and individual users in database management. It adopts a combination of Python and Excel to simplify the operational process and enhance the user interface, thereby reducing the learning curve and technical requirements for managing databases. Users can effortlessly manage their databases, leading to improved work efficiency. This project breaks through the limitations of traditional database management software, offering a simple, efficient, and low-resource solution.

A learned cost model for big data query processing

The efficiency of query processing in the Spark SQL big data processing engine is significantly affected by execution plans and allocated resources. However, existing cost models for Spark SQL rely on hand-crafted rules. While learning-based cost models have been proposed for relational databases, they do not consider available resources. To address this issue, we propose a resource-aware deep learning model capable of automatically predicting query plan execution times based on historical data. To train our model, we embed query execution plans within a query plan tree and extracted features from allocated resources. An adaptive attention mechanism is integrated into the deep learning model to enhance prediction accuracy. Additionally, we extract sufficient features to represent data information and learn the effect of the data on query execution. This approach reduces the need for model retraining owing to data changes. The experimental results demonstrate that our deep cost model outperforms traditional rule-based methods and relational database learning-based optimizers in predicting query plan execution times.

Unleashing real-time analytics: A comparative study of in-memory computing vs. traditional disk-based systems

The article presents a comprehensive study evaluating the performance differences between in-memory computing (IMC) and traditional disk-based database systems, specifically focusing on Redis and PostgreSQL. Given the escalating demands for real-time data analytics across various sectors, the research delves into the comparative efficiency of these two data management paradigms in processing large datasets. Utilizing a synthetic dataset of 23.6 million records, we orchestrated a series of data manipulation tasks, including aggregation, table joins, and filtering operations, to simulate real-world data analytics scenarios. The experiment, conducted on a high-performance computing setup, revealed that Redis significantly outperformed PostgreSQL in all tested operations, showcasing the inherent advantages of IMC in terms of speed and efficiency. Data aggregation tasks saw Redis completing the process up to ten times faster than PostgreSQL. Similarly, table joining, and data filtering tasks were executed more swiftly on Redis, emphasizing IMC's potential to facilitate instantaneous data analytics. These findings underscore the pivotal role of IMC technologies like Redis in empowering organizations to harness real-time insights from big data, a critical capability in today's fast-paced business environment. The study further discusses the implications of adopting IMC over traditional systems, considering aspects such as cost, integration challenges, and the importance of skill development for IT teams. Concluding with strategic recommendations, the article advocates for a nuanced approach to incorporating IMC technologies, highlighting their transformative potential while acknowledging the need for balanced investment and operational planning.

Elucidating the mechanism of action of Isobavachalcone induced autophagy and apoptosis in non-small cell lung cancer by network pharmacology and experimental validation methods

BackgroundLung cancer is the leading cause of cancer deaths, and non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer-related mortality. In recent years, there have been numerous treatments for non-small cell lung cancer, but the cure and survival rates are still extremely low. Isobavachalcone (IBC) belongs to the chalcone component of the traditional Chinese medicine Psoralea corylifolia L., and is a unique Protein kinase B (AKT) pathway inhibitor with significant anticancer effects. Previous studies have shown that IBC possess a variety of biological properties, including anti-cancer, anti-inflammatory, and antioxidant properties. This study focused on the use of network pharmacology analysis, molecular docking technology and experimental validation to elucidate the potential mechanisms of IBC for the treatment of NSCLC. MethodsScreening key genes and pathways of IBC action in NSCLC using network pharmacology. The IBC target genes were from The Encyclopedia of Traditional Chinese Medicine (ETCM) and BATMAN-TCM databases, the NSCLC target genes were from GeneCards, Online Mendelian Inheritance in Man (OMIM) and The Therapeutic Target database (TTD) databases, both of which were taken as intersecting genes for protein–protein interaction network analysis and enrichment analysis, and the binding energies of the compounds to the core targets were further verified by molecular docking. Cell lines in vitro experiments were then performed to further unravel the mechanism of IBC for NSCLC. ResultsA total of 279 potential targets were retrieved by searching the intersection of IBC and NSCLC targets. Protein-protein interaction (PPI) network analysis indicated that 6 targets, including AKT1, RXRA, NCOA1, RXRB, RARA, PPARG were hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that IBC treatment of NSCLC mainly involves steroid binding, transcription factor activity, Pathways in cancer, cAMP signaling pathway, Adenosine 5‘-monophosphate (AMP)-activated protein kinase (AMPK) signaling pathway. Among them, the AMPK signaling pathway, which contained the largest number of enriched genes, may play a greater role in the treatment of NSCLC. Then, the results of in vitro experiment indicated that IBC could inhibit proliferation of NSCLC cells and induce cell autophagy and apoptosis. The results also showed that IBC could increase the protein expression of AMPK and decrease the protein expression of AKT and mammalian target of rapamycin (mTOR), suggesting that IBC can treat NSCLC by inducing cellular autophagy and apoptosis as well as modulating AMPK and AKT signaling pathways. ConclusionsIn summary, this study provided a new insight into the protective mechanism of IBC against NSCLC through network pharmacology and experimental validation.

Analysis of the Mechanism of Intervention of Fangxiangxiaozhi Prescription on Dyslipidemia Based on Network Pharmacology and Molecular Docking

Objective: To explore the mechanism of intervention of Fangxiangxiaozhi prescription on dyslipidemia by using network pharmacology and molecular docking. Methods: The traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Pubchem, Uniprot, and other databases were adopted to screen the active ingredients and the corresponding targets of Fangxiangxiaozhi prescription. Dyslipidemia-related targets were identified using the databases of Disgenet and GeneCards. Then, the intersection target of drugs and diseases was demonstrated via a Venn diagram. Cytoscape3.7.2 was used to construct a “drugs-active ingredients-intersection targets” network map and the key active ingredients with the top 7-degree values were determined. The protein interaction network and topology analysis of the intersection target genes were carried out by combining STRING11.0 and Cytoscape3.7.2. Moreover, the gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the intersection target genes were carried out using the Metascape database. Lastly, the key active ingredients and targets were molecular docked by AutoDockTools, Pymol, and other software. Results: There were 51 active components and 509 target genes of which 74 intersect with dyslipidemia. The key targets included tumor necrosis factor (TNF), interleukin-6 (IL-6), AKT1, PPAR gamma (PPARG), VEGFA, and PPARα. GO enrichment analysis obtained 1040 biological processes, 33 cell components, and 84 molecular functions; KEGG enrichment analysis obtained 148 pathways. The molecular docking results showed that the key targets and compounds exhibited good binding force. Conclusion: The active ingredients of Fangxiangxiaozhi prescription regulated several pathways through multiple targets to intervene in dyslipidemia. This study can serve as a foundation for further research.

Scalability Performance Analysis of Blockchain Using Hierarchical Model in Healthcare.

Blockchain technology has become crucial in improving the privacy and security of enterprise applications in the cyber world. However, scalability has become a significant concern for researchers in large organizations, especially those with complex hierarchies and access privileges. As a result, the existing models and consensus algorithms suffer from various issues. Medical centers and healthcare providers are particularly affected by this problem due to the vast amount of data, making it a critical weakness of traditional database management systems. To address this issue, the authors propose a hierarchical model within the Hyperledger Fabric enterprise application, focusing on the healthcare sector as a use case. This model includes multiple organizations at different levels of the hierarchy, such as hospitals, hospital governance, and insurance companies. The initial implementation of this model includes two levels of hierarchy, demonstrating networks of hospitals joining an insurance company. The primary objective of the experiment is to test and improve the network's performance using this model. The model's performance is evaluated by manipulating and scaling environmental factors such as the number of organizations, transaction numbers, channels, block intervals, and block sizes. The benchmarking tool used for this assessment is Hyperledger Caliper, which measures indicators such as success and failure rates, throughput, and latency. Currently, the research focuses only on testing the model's scalability using patient data.

Evaluation of the mechanism of Gong Ying San activity on dairy cows mastitis by network pharmacology and metabolomics analysis.

The goal of this investigation was to identify the main compounds and the pharmacological mechanism of the traditional Chinese medicine formulation, Gong Ying San (GYS), by infrared spectral absorption characteristics, metabolomics, network pharmacology, and molecular-docking analysis for mastitis. The antibacterial and antioxidant activities were determined in vitro. The chemical constituents of GYS were detected by ultra-high-performance liquid chromatography Q-extractive mass spectrometry (UHPLC-QE-MS). Related compounds were screened from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http://tcmspw.com/tcmsp.php) and the Encyclopedia of Traditional Chinese Medicine (ETCM, http://www.tcmip.cn/ETCM/index.php/Home/) databases; genes associated with mastitis were identified in DisGENT. A protein-protein interaction (PPI) network was generated using STRING. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment screening was conducted using the R module. Molecular-docking analyses were performed with the AutoDockTools V1.5.6. Fifty-four possible compounds in GYS with forty likely targets were found. The compound-target-network analysis showed that five of the ingredients, quercetin, luteolin, kaempferol, beta-sitosterol, and stigmasterol, had degree values >41.6, and the genes TNF, IL-6, IL-1β, ICAM1, CXCL8, CRP, IFNG, TP53, IL-2, and TGFB1 were core targets in the network. Enrichment analysis revealed that pathways associated with cancer, lipids, atherosclerosis, and PI3K-Akt signaling pathways may be critical in the pharmacology network. Molecular-docking data supported the hypothesis that quercetin and luteolin interacted well with TNF-α and IL-6. An integrative investigation based on a bioinformatics-network topology provided new insights into the synergistic, multicomponent mechanisms of GYS's anti-inflammatory, antibacterial, and antioxidant activities. It revealed novel possibilities for developing new combination medications for reducing mastitis and its complications.

Active substance and mechanisms of Actinidia chinensis Planch for the treatment of breast cancer was explored based on network pharmacology and in silico study.

In this paper, our objective was to investigate the potential mechanisms of Actinidia chinensis Planch (ACP) for breast cancer treatment with the application of network pharmacology, molecular docking, and molecular dynamics. "Mihoutaogen" was used as a key word to query the Traditional Chinese Medicine Systems Pharmacology database for putative ingredients of ACP and its related targets. DrugBank, GeneCards, Online Mendelian Inheritance in Man, and therapeutic target databases were used to search for genes associated with "breast cancer." Using Cytoscape 3.9.0 we then constructed the protein-protein interaction and drug-ingredient-target-disease networks. An enrichment analysis of Kyoto encyclopedia of genes and genomes pathway and gene ontology were performed to exploration of the signaling pathways associated with ACP for breast cancer treatment. Discovery Studio software was applied to molecular docking. Finally, the ligand-receptor complex was subjected to a 50-ns molecular dynamics simulation using the Desmond_2020.4 tools. Six main active ingredients and 176 targets of ACP and 2243 targets of breast cancer were screened. There were 118 intersections of targets for both active ingredients and diseases. Tumor protein P53 (TP53), AKT serine/threonine kinase 1 (AKT1), estrogen receptor 1 (ESR1), Erb-B2 receptor tyrosine kinase 2 (ERBB2), epidermal growth factor receptor (EGFR), Jun Proto-Oncogene (JUN), and Heat Shock Protein 90 Alpha Family Class A Member 1 (HSP90AA1) selected as the most important genes were used for verification by molecular docking and molecular dynamics simulation. The primary active compounds of ACP against breast cancer were predicted preliminarily, and its mechanism was studied, thereby providing a theoretical basis for future clinical studies.

Investigating the Mechanistic of Danhong Injection in Brain Damage Caused by Cardiac I/R Injury via Bioinformatics, Computer Simulation, and Experimental Validation.

Cardiac ischemia-reperfusion (I/R) injury has negative effects on the brain and can even lead to the occurrence of ischemic stroke. Clinical evidence shows that Danhong injection (DHI) protects the heart and brain following ischemic events. This study investigated the mechanisms and key active compounds underlying the therapeutic effect of DHI against brain damage induced by cardiac I/R injury. The gene expression omnibus database provided GSE66360 and GSE22255 data sets. The R programming language was used to identify the common differentially expressed genes (cDEGs). Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed, and protein-protein interaction network was constructed. Active compounds of DHI were collected from the Traditional Chinese Medicine Systems Pharmacology database. Molecular docking and molecular dynamics simulations were performed. The MMPBSA method was used to calculate the binding-free energy. The pkCSM server and DruLiTo software were used for Absorption, Distribution, metabolism, excretion, and toxicity (ADMET) analysis and drug-likeness analysis. Finally, in vitro experiments were conducted to validate the results. A total of 27 cDEGs had been identified. The PPI and enrichment results indicated that TNF-α was considered to be the core target. A total of 80 active compounds were retrieved. The molecular docking results indicated that tanshinone I (TSI), tanshinone IIA (TSIIA), and hydroxyl safflower yellow A (HSYA) were selected as core active compounds. Molecular dynamics verification revealed that the conformations were relatively stable without significant fluctuations. MMPBSA analysis revealed that the binding energies of TSI, TSIIA, and HSYA with TNF-α were -36.01, -21.71, and -14.80 kcal/mol, respectively. LEU57 residue of TNF-α has the highest contribution. TSI and TSIIA passed both the ADMET analysis and drug-likeness screening, whereas HSYA did not. Experimental verification confirmed that DHI and TSIIA reduced the expression of TNF-α, NLRP3, and IL-1β in the injured H9C2 and rat brain microvascular endothelial cells. TNF-α can be considered to be a key target for BD-CI/R. TSIIA in DHI exerts a significant inhibitory effect on the inflammatory damage of BD-CI/R, providing new insights for future drug development.

Benefits of Running Databases on Amazon Relational Database Service over Traditional On-premises Servers

Amazon RDS is a web service provided by Amazon to provide easy way to operate and scale relational database in traditional database to cloud. In Traditional or on-premises server, CPU, memory, storage comes together as a bundle. RDS stipulate managed database service for handling most management tasks. After elimination of manual task which is most tedious part ,RDS focus on application and users. This paper delves the migration of database to the cloud. The study highlights introduction to amazon RDS and its comparative study with traditional database. It also throws a light on comparative analysis like cost,performance,security ,maintenance etc by taking use cases.

Network pharmacology and molecular docking analysis on the mechanism of Tripterygium wilfordii Hook in the treatment of Sjögren syndrome.

Tripterygium wilfordii Hook. F (TWH) has significant anti-inflammatory and immunosuppressive effects, and is widely used in the inflammatory response mediated by autoimmune diseases. However, the multi-target mechanism of TWH action in Sjögren syndrome (SS) remains unclear. Therefore, the aim of this study was to explore the molecular mechanism of TWH in the treatment of SS using network pharmacology and molecular docking methods. TWH active components and target proteins were screened from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. SS-related targets were obtained from the GeneCards database. After overlap, the therapeutic targets of TWH in the treatment of SS were screened. Protein-protein interaction and core target analysis were performed by STRING network platform and Cytoscape software. In addition, the affinity between TWH and the disease target was confirmed by molecular docking. Finally, the DAVID (visualization and integrated) database was used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of overlapping targets. The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform database shows that TWH contains 30 active components for the treatment of SS. Protein-protein interaction and core target analysis suggested that TNF, MMP9, TGFB1, AKT1, and BCL2 were the key targets of TWH in the treatment of SS. In addition, the molecular docking method confirmed that the bioactive molecules of TWH had a high affinity with the target of SS. Enrichment analysis showed that TWH active components were involved in multiple signaling pathways. Pathways in cancer, Lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications is the main pathway. It is associated with a variety of biological processes such as inflammation, apoptosis, immune injury, and cancer. Based on data mining network pharmacology, and molecular docking method validation, TWH is likely to be a promising candidate for the treatment of SS drug, but still need to be further verified experiment.

Potential Targets and Signaling Mechanisms of Cinnamaldehyde Enhancing Intestinal Function and Nutritional Regulation in Fat Greenling (Hexagrammos otakii)

Cinnamaldehyde is an ideal feed additive with good immune enhancement and anti-inflammatory regulation effects. However, the anti-inflammatory regulation mechanism in fat greenling (Hexagrammos otakii, H. otakii) remains unclear. The nine targets of cinnamaldehyde were gathered in identified by the Traditional Chinese Medicine Systems Pharmacology database and Uniprot database, and 1,320 intestinal inflammation disease (IIF)-related proteins were screened from DrugBank, Online Mendelian Inheritance in Man (OMIM), Genecards, and Pharmacogenetics and Pharmacogenomics Knowledge Base (PHARMGKB) Databases. According to the Gene Ontology enrichment results and Kyoto Encyclopedia of Genes and Genomes pathway results, cinnamaldehyde may regulated the responses to bacteria, lipopolysaccharide, an inflammatory cytokine, and external stimuli via the nuclear factor kappa-B (NFκB) signaling pathway within on inflammatory network. In addition, the protein–protein interaction analysis assisted in obtaining the closely related inflammatory regulatory proteins, including the C5a anaphylatoxin chemotactic receptor 1 (C5aR1), transcription factor p65 (RELA), prostaglandin G/H synthase 2 (PTGS2), and toll-like receptor 4 (TLR4), which were confirmed as the bottleneck nodes of the network to be more deeply verified via the molecular docking. Moreover, a cinnamaldehyde feeding model was established for evaluating the anti-inflammatory effect of cinnamaldehyde in vivo. According to the current findings implied that cinnamaldehyde may play a protective role against IIF H. otakii by reducing inflammation through the C5 complement (C5)/C5aR1/interleukin-6 (IL-6) and TLR4/NFκB/PTGS2 pathway. The study focused on investigating the action mechanism of cinnamaldehyde on IIF through combining pharmacology and experimental verification in vivo, which provided a fresh perspective on the promoting effect of cinnamaldehyde on IIF in fish.

Multitarget Mechanism of Artemisia Plants to Fight Respiratory Tract Infections Based on Network Pharmacology and Molecular Docking

Objective: To study the mechanism of Artemisia in the treatment of respiratory tract infection (RTI) by network pharmacology and molecular docking. Methods: The active constituents and targets of Artemisia were screened by the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Gene Cards database to obtain disease targets associated with RTI. The key targets of intersection were used to construct the protein–protein interaction (PPI) network, and the hub gene was obtained through topological analysis to construct the pharmacology regulation network of traditional Chinese medicine. The key targets were analyzed by R language enrichment, and the regulatory network diagram of the key target function/pathway was constructed. Molecular docking verifies the binding of the drug to the target predicted by network pharmacology. Results: 144 active components of Artemisia regulated 133 target proteins related to RTI, the core components were β-sitosterol, quercetin, isorhamnetin, artemisinin, etc. Gene ontology enrichment analysis showed that Artemisia regulates receptor ligand activity, cytokine activity, and other molecular functions by acting on membrane region, membrane raft and other cellular structures in the treatment of RTI, thus affecting biological processes such as the response to drugs and bacteria-derived molecules. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that phosphatidylinositol 3-kinase (PI3K)-protein kinase B (Akt), tumor necrosis factor, interleukin-17 (IL-17) signaling pathway was the main pathway for RTI treatment. The molecular docking method confirmed the high affinity between the active ingredient and the RTI target. Conclusions: Artemisia species, a multitarget medication, has been shown to be a viable therapy option for RTI by network pharmacology techniques based on data mining and molecular docking approaches.