Database Software Research Articles

Models for Test Cost Minimization in Database Migration

Database migration is a ubiquitous need faced by enterprises that generate and use vast amounts of data. This is because of database software updates, or it is from changes to hardware, project standards, and other business factors. Migrating a large collection of databases is a way more challenging task than migrating a single database because of the presence of additional constraints. These constraints include capacities of shifts and sizes of databases. In this paper, we present a comprehensive framework that can be used to model database migration problems of different enterprises with customized constraints by appropriately instantiating the parameters of the framework. These parameters are the size of each database, the size of each shift, and the cost of testing each application. Each of these parameters can be either constant or arbitrary. Additionally, the cost of testing an application can be proportional to the number of databases that the application uses. We establish the computational complexities of a number of instantiations of this framework. We present fixed-parameter intractability results for various relevant parameters of the database migration problem. We also provide approximability and inapproximability results as well as lower bounds for the running time of any exact algorithm for the database migration problem. We show that the database migration problem is equivalent to a variation of the classical hypergraph partitioning problem. Our theoretical results also imply new theoretical results for the hypergraph partitioning problem that are interesting in their own right. Finally, we adapt heuristic algorithms devised for the hypergraph partitioning problem to the database migration problem, and we also give experimental results for the adapted heuristics. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: B. Caskurlu and U. U. Acikalin are supported by The Scientific and Technological Research Council of Türkiye [Grant 122E599]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0021 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0021 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Qualitative and Quantitative Analysis of Chemical Components in Yinhua Pinggan Granule with High-Performance Liquid Chromatography Coupled with Q-Exactive Mass Spectrometry.

Yinhua Pinggan Granule (YPG) is an approved compounded traditional Chinese medicine (TCM) prescription for the treatment of cold, cough, viral pneumonia, and related diseases. Due to its complicated chemical composition, the material basis of YPG has not been systematically investigated. In this study, an analytical method based on high-performance liquid chromatography (HPLC) coupled with Q-Exactive mass spectrometry was established. Together with the help of a self-built compound database and Compound Discoverer software 3.1, the chemical components in YPG were tentatively identified. Subsequently, six main components in YPG were quantitatively characterized with a high-performance liquid chromatography-diode array detector (HPLC-DAD) method. As a result, 380 components were annotated, including 19 alkaloids, 8 organic acids, 36 phenolic acids, 27 other phenols, 114 flavonoids, 75 flavonoid glycoside, 72 terpenes, 11 anthraquinones, and 18 other compounds. Six main components, namely, chlorogenic acid, puerarin, 3'-methoxypuerarin, polydatin, glycyrrhizic acid, and emodin, were quantified simultaneously. The calibration curves of all six analytes showed good linearity (R2 > 0.9990) within the test ranges. The precision, repeatability, stability, and recovery values were all in acceptable ranges. In addition, the total phenol content and DPPH scavenging activity of YPG were also determined. The systematic elucidation of the chemical components in YPG in this study may provide clear chemical information for the quality control and pharmacological research of YPG and related TCM compounded prescriptions.

Analysis and Design of a Box Culvert Using Bentley Culvert Master Software

Box culverts are utilized in situations where natural stream flow intersects with roads and railway lines. This research utilizes a digital elevation model and the water Modeling System software to assess the catchment area of the primary valley and identify the factors contributing to flooding in Qoshtapa City. The study involves an analysis of the existing culvert and generated the necessary data for the design of a new culvert. Despite the presence of the existing culvert, floodwater levels rose to over 1 m above the roadway elevation of Erbil-Kirkuk during the last flood event in 2021–2022. The research collected hydrological and climatic data for the study area, conducted soil type analysis using the Harmonized World Soil Database software, and performed hydraulic calculations to estimate the maximum flood discharge of the valley using the Hydrological Engineering Center-Hydrological Modeling System software for flood return periods of 50, 100, and 200 years, for design, to select the best economic alternative. The new culvert design was executed using Bentley Culvert Master software to ensure that floodwaters can flow through the culvert without rising to street level. The results indicated that the new culvert design surpasses the capacity of the existing one. The results show that the best economic alternative hydraulic design is the first alternative capacity of 201 m3/s of a 100-year return period; the new design cross-section area of the culvert is 52.5 m2.

Performance evaluation of hybrid fiber reinforced concrete on engineering properties and life cycle assessment: A sustainable approach

Conventional concrete in its existing form possesses characteristics like better compressive strength, density, resistance against moisture and enhanced performance under various exposure conditions like elevated temperature and long-term durability etc. However, some of the shortcomings such as reduced ductility, brittleness can be improved by using additives like fibers to improve its properties. The present study highlights the performance evaluation of engineering properties and life cycle assessment (LCA) of hybrid fiber reinforced concrete (HFRC) incorporating with glass and polypropylene fibers in equal proportions (0%, 0.5%, 1.0%, 1.5%, and 2.0%). In addition to these, impact resistance at first crack phase and post cracking phase i.e., at failure have also been investigated to ascertain the effect of HFRC vs conventional concrete mixes. Sorptivity studies also performed to find its effect on HFRC, and control concrete mixes prepared with and without glass and polypropylene fibers. Reduction in workability of concrete was observed with increase in hybrid fiber content. Superior performance in terms of strength and durability properties was observed at 1% hybrid fiber content. Similarly, this study also focuses on studying the impact of HFRC and conventional concrete on environment using LCA approach. For LCA, SimaPro software and EcoInvent database is used to compare environmental impact assessment of HFRC and conventional concrete mixes. Impact on environment and cost comparison on HFRC mixes exhibits satisfactory results when compared to conventional mix. Finally, this research will address the need for HFRC mixes to achieve their adaptability in construction sector to make concrete economic and environmentally friendly.

Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data.

The advent of high-throughput sequencing technologies has not only revolutionized the field of bioinformatics but has also heightened the demand for efficient taxonomic classification. Despite technological advancements, efficiently processing and analyzing the deluge of sequencing data for precise taxonomic classification remains a formidable challenge. Existing classification approaches primarily fall into two categories, database-based methods and machine learning methods, each presenting its own set of challenges and advantages. On this basis, the aim of our study was to conduct a comparative analysis between these two methods while also investigating the merits of integrating multiple database-based methods. Through an in-depth comparative study, we evaluated the performance of both methodological categories in taxonomic classification by utilizing simulated data sets. Our analysis revealed that database-based methods excel in classification accuracy when backed by a rich and comprehensive reference database. Conversely, while machine learning methods show superior performance in scenarios where reference sequences are sparse or lacking, they generally show inferior performance compared with database methods under most conditions. Moreover, our study confirms that integrating multiple database-based methods does, in fact, enhance classification accuracy. These findings shed new light on the taxonomic classification of high-throughput sequencing data and bear substantial implications for the future development of computational biology. For those interested in further exploring our methods, the source code of this study is publicly available on https://github.com/LoadStar822/Genome-Classifier-Performance-Evaluator. Additionally, a dedicated webpage showcasing our collected database, data sets, and various classification software can be found at http://lab.malab.cn/~tqz/project/taxonomic/.

Active substances and molecular mechanisms of Astragali Radix and Angelicae Sinensis radix against idiopathic pulmonary fibrosis effects by network pharmacology and in vitro experiments

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic disease with an unclear etiology and no effective treatment. This study aims to elucidate the pathogenic mechanism networks involving multiple targets and pathways in IPF. Extracts and metabolites of Astragali Radix (AR) and Angelicae Sinensis Radix (ASR), two well-known traditional Chinese medicines, have demonstrated therapeutic effects on IPF. However, the underlying mechanisms of AR and ASR remain unclear. Utilizing network pharmacology analysis, the disease targets associated with IPF were obtained from the GeneCards database and Online Mendelian Inheritance in Man (OMIM) database. Targets of AR and ASR were identified using the Traditional Chinese Medicine (TCM) Systems Pharmacology Database and Analysis Platform and Swiss Target Prediction. A proteinprotein interaction (PPI) network was subsequently constructed and analyzed using the STRING database and Cytoscape software. Gene ontology enrichment (GO) analysis and kyoto encyclopedia of genes and genomes (KEGG) analysis were conducted using DAVID. Additionally, a component-target-pathway network was employed to identify the main active components, and molecular docking was performed between these components and proteins encoded by key targets. Finally, in vitro studies were conducted based on network pharmacology. A total of 260 common targets between IPF and drug targets were identified and included in the PPI network, in which TNF, IL-6, STAT3, AKT1, VEGFA, SRC, EGFR, INS, JUN, and IL1B were predicted as key targets. These 260 targets were enriched in the PI3K-AKT signaling pathway, HIF-1 signaling pathway, TNF signaling pathway, MAPK signaling pathway, FOXO signaling pathway, and Pathways in cancer. Docking scores ranged from –4.1 to –9.5 kcal/mol, indicating a strong binding affinity between the main active compounds and key targets. In vitro studies have indeed shown that Quercetin and Magnolol can alleviate the expression of epithelial-mesenchymal transition in the A549 cells caused by IL-6. The treatment with AR and ASR resulted in a reduction of mRNA levels for key targets HIF-1α and α-SAM. Additionally, the protein expression levels of P-JAK2/JAK2, P-STAT3/STAT3, and α-SMA/β-Actin were also reduced. These results support the therapeutic potential of AR and ASR in ameliorating pulmonary fibrosis and provide insight into the molecular mechanisms involved in their therapeutic effects.

A bioinformatics approach of specificity protein transcription factors in head and neck squamous cell carcinoma.

The seventh most common type of cancer with increasing diagnosis rates around the world is head and neck squamous cell carcinoma (HNSCC). Specificity proteins (SPs) have been known for their role in the regulation of cellular division, growth, and apoptotic pathways in various cancers. In this work, we analyzed the expression levels of SPs in HNSCC to assess their diagnostic and prognostic biomarker potential. Differential gene expression and correlation analysis methods were used to determine the top dysregulated genes in HNSCC. Functional enrichment and protein-protein interaction analyses were done with the DAVID database and Cytoscape software to understand their function and biological processes. Receiver operating test, logistic regression, and Cox regression analyses were performed to check SP genes' diagnostic and prognostic potential. SP1 (LogFC = -0.27, P = 0.0013) and SP2 (LogFC = -0.20, P = 0.0019) genes were upregulated in HNSCC samples, while SP8 (LogFC = 2.57, P < 0.001) and SP9 (LogFC = 2.57, P < 0.001) genes were downregulated in cancer samples. A moderate positive correlation was observed among the expression levels of SP1, SP2, and SP3 genes. The SP8 and SP9 genes with AUC values of 0.79 and 0.75 demonstrated diagnostic potential which increased to 0.84 when both genes were assessed by logistic regression test. Also, the SP1 gene held a marginally significant prognostic potential. Our findings clarify the potential of SP transcription factors as candidate diagnostic and prognostic biomarkers for early screening and treatment of HNSCC.

UPLC-Q-TOF-MSE Based Profiling of the Chemical Composition of Different Parts of Tetrastigma hemsleyanum

Objectives: The differences in chemical constituents in different parts of Tetrastigma hemsleyanum were analyzed. Methods: The contents of total phenols, total flavonoids, and polysaccharides in methanol extracts from different parts of T. hemsleyanum in Zhejiang were determined, and the chemical components of different parts of T. hemsleyanum were collected by ultra-performance liquid chromatography-tandem quadrupole-time-of-flight mass spectrometry. The differential index components of different parts of T. hemsleyanum were identified and analyzed by accurate mass-to-charge ratio of primary mass spectrometry and fragment ion of secondary mass spectrometry with reference spectrum and software database search and related literature reports. Results: A total of 20 components were identified, including 13 identified in root tubers, 7 in fibrous roots and 8 in leaves, and the structural cleavage of its compounds was analyzed. Conclusion: It provides a possibility and theoretical basis for the future use of the whole plant of T. hemsleyanum as medicine.

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

Identification of potential hub genes as biomarkers for breast, ovarian, and endometrial cancers

Breast cancer (BC) and gynecological cancers have emerged as significant threats to women’s health and are known to be among the primary causes of cancer-related fatalities in women. Innovative treatments and early detection may significantly cut mortality rates for these diseases. In this study, potential hub genes were thoroughly evaluated in the contexts of BC, ovarian cancer (OC), and endometrial cancer (EC). Initially, a total of 374 overlapping differentially expressed genes (DEGs) were identified within the microarray datasets. The STRING database and Cytoscape software analyzed protein-protein interaction (PPI) network structure, whereas FunRich found hub genes. The five hub genes that were ultimately discovered are PTEN, SMAD2, FASN, CYCS, and KRAS. As a result, these genes may serve as potential biomarkers for the aforementioned diseases. Importantly, this study offers valuable insights into all three diseases based on recent molecular advancements. However, further investigation is required to precisely measure these biomarkers’ effectiveness.

An international survey of contemporary practices towards fertility assessment and preservation in patients undergoing radical inguinal orchidectomy for testicular cancer.

The study aims to assess current international clinician attitudes, practices and barriers towards fertility assessment and preservation in patients undergoing radical inguinal orchidectomy (RIO) for testicular cancer. An international online survey of urologists and urologists in training who perform RIO for testicular cancer was developed by the British Association of Urological Surgeons (BAUS) Sections of Andrology and Oncology and the British Urology Researchers in Surgical Training (BURST). The recruitment process used social media and the emailing lists of national urological societies. Responses were collected between 10/02/2021 and 31/05/2021 and stored using password-protected Research Electronic Data Capture (REDCap) database software. The primary outcome was the proportion of urologists who routinely offer semen cryopreservation prior to RIO. The study was reported according to the Checklist for Reporting Results of Internet E-Surveys platform. A total of 393 respondents took part in the online survey; of these, the majority were from the United Kingdom (65.9%), with the remaining international respondents (34.1%) from six different continents, which included 45 different countries. Of the respondents, 57.1% reported that they would routinely offer semen cryopreservation to all patients undergoing RIO for testicular cancer. In addition, 36.0% of urologists routinely performed pre-operative semen analysis, and 22.1% routinely performed pre-operative testicular serum hormone profile. Of the respondents, 14.4% performed expedited RIO within 48 h; 31.2% of respondents reported that they considered no delay to RIO to allow for semen cryopreservation to be acceptable. A significant proportion of international urologists do not offer pre-operative fertility assessment and preservation in men undergoing RIO for testicular cancer. Surgery is performed in an expedited fashion within 1 week in the majority of patients. Urologists perceive there to be a lack of access and availability to fertility services, and that delay to RIO to allow for fertility preservation is often not acceptable.

Elucidating the interplay of ferroptosis-related genes in keloid formation: Insights from bioinformatics analysis

BackgroundKeloids are benign skin tumors characterized by fibroblast proliferation, tumor-like biological behavior, and excessive deposition of extracellular matrix in wounded skin. Ferroptosis, a type of programmed cell death, is critical in tumor pathogenesis. We aimed to investigate the role of ferroptosis in keloid formation. MethodsWe downloaded public high-throughput sequencing raw count data (GSE92566), containing three normal skin and four keloid samples, from the Gene Expression Omnibus database. Ferroptosis-related genes were obtained from the Ferroptosis database website. The ferroptosis-related differentially expressed genes (FRDEGs) were obtained by merging differentially expressed genes with ferroptosis-related genes. The FRDEGs were then used for Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Gene Set Enrichment Analysis, protein-protein interaction (PPI) network, and microRNA (miRNA)-mRNA network analysis. Finally, real-time quantitative polymerase chain reaction (RT-qPCR) was performed to validate our findings. ResultsWe found 25 FRDEGs, including 8 up-regulated and 17 down-regulated genes. Pathway enrichment analysis revealed that the Hippo and transforming growth factor β signaling pathways were significantly up-regulated in keloids. In contrast, regulation of the peroxisome proliferator-activated receptor signaling pathway, glutathione metabolism, and unsaturated fatty acid metabolic process were down-regulated. PPI and FRDEGs hub networks were constructed using the STRING database and Cytoscape software. Ten hub genes were identified, including PLA2G6, RARRES2, SNCA, CYP4F8, CDKN2A, ALOX12, FABP4, ALOX12B, NEDD4, and NEDD4L. We constructed a miRNA-mRNA network, which predicted hsa-mir-155-5p, hsa-let-7b-5p, hsa-mir-124-3p, hsa-mir-145-5p, hsa-mir-328-3p, hsa-mir-24-3p, and hsa-mir-10b-5p as the most connected miRNAs regulating ferroptosis in keloids. Finally, we verified the expression levels of the hub genes by RT-qPCR, which confirmed that ALOX12, ALOX12B, and CYP4F8 expression were reduced in keloids. ConclusionsThis study provides novel information on ferroptosis-mediated keloid pathogenesis, underscoring the importance of further research in this area to unlock new therapeutic avenues for keloid treatment.

Effectiveness of Graptophyllum pictum L. Griff Extract as a Complementary Therapy for Peri-Implantitis: An In Vitro and In Silico Study

Background: Dental peri-implantitis is an inflammation that occurs in the peri-implant soft tissue and is accompanied by bone resorption, decreased osseointegration, and pockets. Graptophyllum pictum L. Griff extract may act as complementary therapy for peri-implantitis. Objectives: To investigate the effectiveness of G. pictum L. Griff extract as a complementary therapy for peri-implantitis. Materials and Methods: A phytochemical test was performed to determine the active compounds using each compound's reagents. Antioxidant assay to examine the antioxidant activity compared with vitamin C. Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) tests were used to investigate the antibacterial activity of Aggregatibacter actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), Fusobacterium nucleatum (Fn), and Provotella intermedia (Pi) as peri-implantitis-related bacteria. Molecular docking is an in-silico test to visualize the interaction of G. pictum L. Griff molecules with targeted proteins using several software programs and databases. This study was analyzed by parametric and non-parametric variance analysis and continued with a post-hoc test. Results: G. pictum L. Griff extract contained flavonoids, alkaloids, saponins, tannins, steroids, and terpenoids. The antibacterial effect of G. pictum L. Griff can inhibit the growth of dental peri-implantitis-related bacteria such as Aa, Pg, Pi, and Fn with a minimum 25% extract concentration. In silico, the interaction of ethylcholest and proteins shows possible pharmalogical effects such as anti-inflammatory, antibacterial, and antioxidant activity. Conclusion: G. pictum L. Griff extract possesses good antibacterial, antioxidant, and anti-inflammatory activity that may be effective for dental peri-implantitis complementary therapy as documented in silico and in vitro.

Untargeted metabolomics based on ultra-high performance liquid chromatography-mass spectrometry/MS reveals the lipid-lowering mechanism of taurine in hyperlipidemia mice.

Taurine has a prominent lipid-lowering effect on hyperlipidemia. However, a comprehensive analysis of the effects of taurine on endogenous metabolites in hyperlipidemia has not been documented. This study aimed to explore the impact of taurine on multiple metabolites associated with hyperlipidemia. The hyperlipidemic mouse model was induced by high-fat diet (HFD). Taurine was administered via oral gavage at doses of 700 mg/kg/day for 14 weeks. Evaluation of body weight, serum lipid levels, and histopathology of the liver and adipose tissue was performed to confirm the lipid-lowering effect of taurine. Ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS)-based metabonomics analyses of serum, urine, feces, and liver, coupled with multivariate data analysis, were conducted to assess changes in the endogenous metabolites. Biochemical and histological examinations demonstrated that taurine administration prevented weight gain and dyslipidemia, and alleviated lipid deposition in the liver and adipose tissue in hyperlipidemic mice. A total of 76 differential metabolites were identified by UPLC-MS-based metabolomics approach, mainly involving BAs, GPs, SMs, DGs, TGs, PUFAs and amino acids. Taurine was found to partially prevent HFDinduced abnormalities in the aforementioned metabolites. Using KEGG database and MetaboAnalyst software, it was determined that taurine effectively alleviates metabolic abnormalities caused by HFD, including fatty acid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, diacylglycerol metabolism, amino acid metabolism, bile acid and taurine metabolism, taurine and hypotaurine metabolism. Moreover, DGs, GPs and SMs, and taurine itself may serve as active metabolites in facilitating various anti-hyperlipidemia signal pathways associated with taurine. This study provides new evidence for taurine to prevent hyperlipidemia.

Tribuloside: Mechanisms and Efficacy in Treating Acute Lung Injury Revealed by Network Pharmacology and Experimental Validation.

Acute lung injury (ALI) is a serious illness that has few treatment options available. Tribuloside, a natural flavonoid extracted from the Tribulus Terrestris plant in China, is potent in addressing many health issues such as headaches, dizziness, itching, and vitiligo. This study intends to explore the mechanisms of action of Tribuloside in treating ALI through a combination of network pharmacology and experimental validation. We obtained the 2D structure and SMILES number of Tribuloside from the PubChem database. We used the SwissTargetPrediction database to identify pharmacological targets. We found 1215 targets linked to ALI by examining the GeneCards database. We used the String database and Cytoscape software to create the "drug or disease-target" network as well as the protein-protein interactions (PPI). Key targets were identified by evaluating associated biological processes and pathway enrichment. A Venny Diagram showed 49 intersection points between Tribuloside and ALI. Molecular docking with AutoDockTools found that Tribuloside had a high affinity for IL6, BCL2, TNF, STAT3, IL1B, and MAPK3, the top 6 targets in the PPI network by Degree values. To test Tribuloside's therapeutic efficacy in ALI, an acute lung damage model in mice was constructed using lipopolysaccharide. Tribuloside treatment reduced inflammatory cell infiltration, decreased fibrotic area, repaired damaged alveoli, and suppressed inflammatory factors IL-6, TNF-α, and IL-1β in the lungs through many pathways and targets. This study reveals that Tribuloside has the potential to treat ALI by targeting various pathways and targets, according to network pharmacology predictions and experimental confirmation.

Application of network pharmacology and molecular docking to explore the mechanism of Danggui Liuhuang Tang against hyperthyroidism.

To investigate the mechanism of Danggui Liuhuang Tang (DGLHT) in the treatment of hyperthyroidism (HT), we explored the multi-component, multi-target, and multi-pathway mechanism based on the network pharmacology method of traditional Chinese medicine. To take DGLHT into the blood components as the research object, we used GeneCards, Drungbank, Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM), Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB), and other databases to predict the potential target of the components. Then, it was integrated with the predicted targets of HT disease to obtain the potential targets of DGLHT in the treatment of HT. We used String database and Cytoscape software for protein-protein interaction network (PPI) construction, and DAVID platform for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation, the Cytoscape software was used to construct a "component-target-pathway" network; the AutoDock Vina platform was used to conduct molecular docking between the blood entry components and key targets. According to the analysis, a total of 93 active ingredients, 348 disease-related targets, and 36 potential targets were screened out. Among them, key targets such as MAPK1, CCND1, AKT1, and TNF exert curative effects, and the main pathways are the HIF-1 signaling pathway, FoxO signaling pathway, Chemokine signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, T cell receptor signaling pathway, Jak-STAT signaling pathway, and other pathways. Molecular docking results verified the interaction between active ingredients and key targets, among which rustication and quercetin had high docking affinity with key target proteins MAPK1 and CCND1. This study preliminary revealed that DGLHT has the characteristics of multi-component, multi-target, and multi-pathway in the treatment of HT, and it established a scientific foundation for a more detailed investigation of DGLHT's molecular mechanism in the treatment of HT.

Mechanism and Experimental Verification of Rhodiola rosea L. for the Treatment of Acute Lung Injury Through Network Pharmacology and Molecular Docking

Objective: This article endeavors to dissect the bioactive elements, pharmacological effects, and potential mechanisms of Rhodiola rosea L. (RL) concerning acute lung injury (ALI) using a combined approach of network pharmacology and subsequent in vivo experimental validation. Methods: RL constituents and associated targets were retrieved from the Organchem database and pertinent articles. ALI-related targets were meticulously curated through comprehensive searches of GeneCards, OMIM, and DisGeNET databases. Utilizing Cytoscape 3.8.2 software, an intricate RL-ALI ingredients-targets network diagram was fashioned. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the Metascape database and microbiota software to unravel the functional and pathway implications of the identified interactions. AutoDock Vina and PyMOL were used to conduct molecular docking. Subsequently, the ALI rats were established by intraperitoneal injection of lipopolysaccharide (LPS) into Sprague Dawley rats, the potential mechanism for treating ALI was validated by kaempferol. Results: 18 active components of RL for ALI were screened, which were related to 306 targets of RL for ALI. Kaempferol (Kae) was a potential major component of ALI treatment. Molecular docking showed Kae had good binding ability to Epidermal Growth Factor Receptor (EGFR), nonreceptor tyrosine kinase, and serine/threonine kinase 1. In addition, at the animal level, Kae alleviated ALI by inhibiting the production of inflammatory cytokines. Lung tissue sections of the Kae-treated group at different doses showed reduced bleeding and reduced exudation of inflammatory factors compared to the LPS cohort. Compared with the LPS group, the secretion of Interleukin-6, tumor necrosis factor-alpha, and Phosphorylated Epidermal Growth Factor Receptor was significantly decreased in the Kae group, while the content of Phosphorylated Protein Kinase B was increased, and the lung injury was alleviated. Conclusion: Through network pharmacology and experimental verification, we identified Kae as the main active ingredient. Kae may reduce the expression of inflammatory factors and regulate the EGFR-AKT signaling pathway, subsequently exerting therapeutic effects in the treatment of ALI.

Development and application of matte/speiss/metal thermodynamic database for optimization of processing of drosses, dusts and reverts from lead, zinc and copper production

Detrimental elements such as arsenic and antimony tend to accumulate in dusts and drosses of lead-zinc as well as copper smelters. These by-products are commonly treated in dedicated reductive smelting units producing Cu-rich matte, As-Sb-collecting speiss and Pb-rich metal, as well as liquid slag. Such process would have a complex distribution of elements (Cu, Fe, Pb, Zn, Ni, Sn, Sb, As, Ag, Au, S, etc.) among four liquid phases. Thermodynamic calculations can be used for predictions and optimization of such processes. They must rely on accurate models developed in integration with experimental program. Present study reports results of recent progress in experimental and modelling studies of As, Sb-containing speiss systems and demonstration of calculations relevant for industrial conditions. Experimental methodology consisted of equilibration, quenching and electron probe X-ray microanalysis. Calculations were performed using FactSage software and thermodynamic database developed based on the dedicated recent experiments. The main findings of the paper are: a set of binary and ternary diagrams relevant to matte and speiss formation conditions; distribution of Ag and Au among liquid Pb and speiss in key quaternary systems; and distributions of 10 main elements for industrially relevant matte/speiss/metal multicomponent equilibria.

Potential of semen coicis in enhancing the anti-tumor effects of PD-1 inhibitor on A549 cell lines by blocking the PI3K-AKT-mTOR pathway.

The objective of this research was to investigate how the combination of semen coicis extract and PD-1 inhibitors can potentially work together to enhance the anti-tumor effects, with a focus on understanding the underlying mechanism. We obtained the active components and specific targets of semen coicis in the treatment of NSCLC from various databases, namely TCMSP, GeneCard, and OMIM. By utilizing the STRING database and Cytoscape software, we established a protein interaction network (PPI) for the active ingredient of semen coicis and the target genes related to NSCLC. To explore the potential pathways involved, we conducted gene ontology (GO) and biological pathway (KEGG) enrichment analyses, which were further supported by molecular docking technology. Additionally, we conducted cyto-inhibition experiments to verify the inhibitory effects of semen coicis alone or in combination with a PD-1 inhibitor on A549 cells, along with examining the associated pathways. Furthermore, we investigated the synergistic mechanism of these two drugs through cytokine release experiments and the PD-L1 expression study on A549 cells. Semen coicis contains two main active components, Omaine and (S)-4-Nonanolide. Its primary targets include PIK3R1, PIK3CD, PIK3CA, AKT2, and mTOR. Molecular docking experiments confirmed that these ingredients and targets form stable bonds. In vitro experiments showed that semen coicis demonstrates inhibitory effects against A549 cells, and this effect was further enhanced when combined with PD-1 inhibitors. PCR and WB analysis confirmed that the inhibition of the PI3K-AKT-mTOR pathway may contribute to this effect. Additionally, semen coicis was observed to decrease the levels of IFN-γ, IL-6, and TNF-α, promoting the recovery of the human anti-tumor immune response. And semen coicis could inhibit the induced expression of PD‑L1 of A549 cells stimulated by IFN‑γ as well. Semen coicis not only has the ability to kill tumor cells directly but also alleviates the immunosuppression found in the tumor microenvironment. Additionally, it collaboratively enhances the effectiveness of PD-1 inhibitors against tumors by blocking the activation of PI3K-AKT-mTOR.

Network pharmacology, computational biology integrated surface plasmon resonance technology reveals the mechanism of ellagic acid against rotavirus

The target and mechanism of ellagic acid (EA) against rotavirus (RV) were investigated by network pharmacology, computational biology, and surface plasmon resonance verification. The target of EA was obtained from 11 databases such as HIT and TCMSP, and RV-related targets were obtained from the Gene Cards database. The relevant targets were imported into the Venny platform to draw a Venn diagram, and their intersections were visualized. The protein–protein interaction networks (PPI) were constructed using STRING, DAVID database, and Cytoscape software, and key targets were screened. The target was enriched by Gene Ontology (GO) and KEGG pathway, and the ‘EA anti-RV target-pathway network’ was constructed. Schrodinger Maestro 13.5 software was used for molecular docking to determine the binding free energy and binding mode of ellagic acid and target protein. The Desmond program was used for molecular dynamics simulation. Saturation mutagenesis analysis was performed using Schrodinger's Maestro 13.5 software. Finally, the affinity between ellagic acid and TLR4 protein was investigated by surface plasmon resonance (SPR) experiments. The results of network pharmacological analysis showed that there were 35 intersection proteins, among which Interleukin-1β (IL-1β), Albumin (ALB), Nuclear factor kappa-B1 (NF-κB1), Toll-Like Receptor 4 (TLR4), Tumor necrosis factor alpha (TNF-α), Tumor protein p53 (TP53), Recombinant SMAD family member 3 (SAMD3), Epidermal growth factor (EGF) and Interleukin-4 (IL-4) were potential core targets of EA anti-RV. The GO analysis consists of biological processes (BP), cellular components (CC), and molecular functions (MF). The KEGG pathways with the highest gene count were mainly related to enteritis, cancer, IL-17 signaling pathway, and MAPK signaling pathway. Based on the crystal structure of key targets, the complex structure models of TP53-EA, TLR4-EA, TNF-EA, IL-1β-EA, ALB-EA, NF-κB1-EA, SAMD3-EA, EGF-EA, and IL-4-EA were constructed by molecular docking (XP mode of flexible docking). The MMGBS analysis and molecular dynamics simulation were also studied. The Δaffinity of TP53 was highest in 220 (CYS → TRP), 220 (CYS → TYR), and 220 (CYS → PHE), respectively. The Δaffinity of TLR4 was highest in 136 (THR → TYR), 136 (THR → PHE), and 136 (THR → TRP). The Δaffinity of TNF-α was highest in 150 (VAL → TRP), 18 (ALA → GLU), and 144 (PHE → GLY). SPR results showed that ellagic acid could bind TLR4 protein specifically. TP53, TLR4, and TNF-α are potential targets for EA to exert anti-RV effects, which may ultimately provide theoretical basis and clues for EA to be used as anti-RV drugs by regulating TLR4/NF-κB related pathways.