Traditional Database Research Articles

Mechanism of Polygala-Acorus in Treating Autism Spectrum Disorder Based on Network Pharmacology and Molecular Docking.

Recent epidemic survey data have revealed a globally increasing prevalence of autism spectrum disorders (ASDs). Currently, while Western medicine mostly uses a combination of comprehensive intervention and rehabilitative treatment, patient outcomes remain unsatisfactory. Polygala-Acorus, used as a pair drug, positively affects the brain and kidneys, and can improve intelligence, wisdom, and awareness; however, the underlying mechanism of action is unclear. We performed network pharmacology analysis of the mechanism of Polygala-Acorus in treating ASD and its potential therapeutic effects to provide a scientific basis for the pharmaceutical's clinical application. The chemical compositions and targets corresponding to Polygala-Acorus were obtained using the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform, Chemical Source Website, and PharmMapper database. Disease targets in ASD were screened using the DisGeNET, DrugBank, and GeneCards databases. Gene Ontology functional analysis and metabolic pathway analysis (Kyoto Encyclopedia of Genes and Genomes) were performed using the Metascape database and validated via molecular docking using AutoDock Vina and PyMOL software. Molecular docking analysis showed that the key active components of Polygala- Acorus interacted with the following key targets: EGFR, SRC, MAPK1, and ALB. Thus, the key active components of Polygala-Acorus (sibiricaxanthone A, sibiricaxanthone B tenuifolin, polygalic acid, cycloartenol, and 8-isopentenyl-kaempferol) have been found to bind to EGFR, SRC, MAPK1, and ALB. This study has preliminarily revealed the active ingredients and underlying mechanism of Polygala-Acorus in the treatment of ASD, and our predictions need to be proven by further experimentation.

Network Pharmacology, Molecular Docking and Experimental Verification Revealing the Mechanism of Fule Cream against Childhood Atopic Dermatitis.

The Fule Cream (FLC) is an herbal formula widely used for the treatment of pediatric atopic dermatitis (AD), however, the main active components and functional mechanisms of FLC remain unclear. This study performed an initial exploration of the potential acting mechanisms of FLC in childhood AD treatment through analyses of an AD mouse model using network pharmacology, molecular docking technology, and RNA-seq analysis. The main bioactive ingredients and potential targets of FLC were collected from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and SwissTargetPrediction databases. An herb-compound-target network was built using Cytoscape 3.7.2. The disease targets of pediatric AD were searched in the DisGeNET, Therapeutic Target Database (TTD), OMIM, DrugBank and GeneCards databases. The overlapping targets between the active compounds and the disease were imported into the STRING database for the construction of the protein-protein interaction (PPI) network. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the intersection targets were performed, and molecular docking verification of the core compounds and targets was then performed using AutoDock Vina 1.1.2. The AD mouse model for experimental verification was induced by MC903. The herb-compound-target network included 415 nodes and 1990 edges. Quercetin, luteolin, beta-sitosterol, wogonin, ursolic acid, apigenin, stigmasterol, kaempferol, sitogluside and myricetin were key nodes. The targets with higher degree values were IL-4, IL-10, IL-1α, IL-1β, TNFα, CXCL8, CCL2, CXCL10, CSF2, and IL-6. GO enrichment and KEGG analyses illustrated that important biological functions involved response to extracellular stimulus, regulation of cell adhesion and migration, inflammatory response, cellular response to cytokine stimulus, and cytokine receptor binding. The signaling pathways in the FLC treatment of pediatric AD mainly involve the PI3K-Akt signaling pathway, cytokine‒cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and NF-κB signaling pathway. The binding energy scores of the compounds and targets indicate a good binding activity. Luteolin, quercetin, and kaempferol showed a strong binding activity with TNFα and IL-4. This study illustrates the main bioactive components and potential mechanisms of FLC in the treatment of childhood AD, and provides a basis and reference for subsequent exploration.

Deciphering the Underlying Mechanisms of Sanleng-Ezhu for the Treatment of Idiopathic Pulmonary Fibrosis Based on Network Pharmacology and Single-cell RNA Sequencing Data.

To decipher the underlying mechanisms of Sanleng-Ezhu for the treatment of idiopathic pulmonary fibrosis based on network pharmacology and single-cell RNA sequencing data. Idiopathic Pulmonary Fibrosis (IPF) is the most common type of interstitial lung disease. Although the combination of herbs Sanleng (SL) and Ezhu (EZ) has shown reliable efficacy in the management of IPF, its underlying mechanisms remain unknown. Based on LC-MS/MS analysis and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, we identified the bioactive components of SL-EZ. After obtaining the IPF-related dataset GSE53845 from the Gene Expression Omnibus (GEO) database, we performed the differential expression analysis and the weighted gene co-expression network analysis (WGCNA), respectively. We obtained lowly and highly expressed IPF subtype gene sets by comparing Differentially Expressed Genes (DEGs) with the most significantly negatively and positively related IPF modules in WGCNA. Subsequently, we performed Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on IPF subtype gene sets. The low- and highexpression MCODE subgroup feature genes were identified by the MCODE plug-in and were adopted for Disease Ontology (DO), GO, and KEGG enrichment analyses. Next, we performed the immune cell infiltration analysis of the MCODE subgroup feature genes. Single-cell RNA sequencing analysis demonstrated the cell types which expressed different MCODE subgroup feature genes. Molecular docking and animal experiments validated the effectiveness of SL-EZ in delaying the progression of pulmonary fibrosis. We obtained 5 bioactive components of SL-EZ as well as their corresponding 66 candidate targets. After normalizing the samples of the GSE53845 dataset from the GEO database source, we obtained 1907 DEGs of IPF. Next, we performed a WGCNA analysis on the dataset and got 11 modules. Notably, we obtained 2 IPF subgroups by contrasting the most significantly up- and down-regulated modular genes in IPF with DEGs, respectively. The different IPF subgroups were compared with drugcandidate targets to obtain direct targets of action. After constructing the protein interaction networks between IPF subgroup genes and drug candidate targets, we applied the MCODE plug-in to filter the highest-scoring MCODE components. DO, GO, and KEGG enrichment analyses were applied to drug targets, IPF subgroup genes, and MCODE component signature genes. In addition, we downloaded the single-cell dataset GSE157376 from the GEO database. By performing quality control and dimensionality reduction, we clustered the scattered primary sample cells into 11 clusters and annotated them into 2 cell subtypes. Drug sensitivity analysis suggested that SL-EZ acts on different cell subtypes in IPF subgroups. Molecular docking revealed the mode of interaction between targets and their corresponding components. Animal experiments confirmed the efficacy of SL-EZ. We found SL-EZ acted on epithelial cells mainly through the calcium signaling pathway in the lowly-expressed IPF subtype, while in the highly-expressed IPF subtype, SL-EZ acted on smooth muscle cells mainly through the viral infection, apoptosis, and p53 signaling pathway.

Optimizing warehouse management system with blockchain and machine learning predictive data analytics

Blockchain technology is proving to be a disruptive technology in many areas of supply chain, manufacturing, medical, agriculture, and so on. Warehouses are an inevitable part of the supply chain. Issues like space optimization, route optimization, quick item pick-up, demand forecasting, and transaction management are of importance to address in warehouse management systems (WMS). Traditional database systems have limitations of interoperability among different entities involved in warehouses. This paper presents an innovative application of blockchain technology and machine learning (ML) to build a smart warehouse management system in Web3 (SWMW3). We developed a decentralized application (DApp) using Web3.0 principles, integrating ReactJS for the frontend, express for the backend, and blockchain through smart contracts. This integration enhances security and transparency by storing WMS operational data in the blockchain and automating payments and verifications through smart contracts. Additionally, we implemented a ML model for predicting the total time from order receipt to delivery, leveraging historical data to optimize workflow, reduce delays, and improve overall efficiency. This combination of blockchain for secure transactions and ML for predictive analytics generates a robust, efficient, and optimized management system for the warehouse.

Doctoral students’ reflections on generative artificial intelligence (GenAI) use in the literature review process

ABSTRACT Amidst discussions about the use of Generative AI (GenAI) for academic research, their potential for doctoral research or literature reviews is an area beginning to be explored. This qualitative study explores doctoral students’ perceptions of the value of GenAI tools in the literature review process. The analysis of 26 participants’ reflections on their exploration of three GenAI technologies revealed benefits and challenges for the literature review process. Participants highlighted the potential of these tools as complementary to traditional database searches presuming users possess prior information literacy and research skills. They also perceived several challenges such as inaccuracies, usability and privacy and emphasised the importance and indispensability of a human review. This study highlights the need for guidance and strategies for doctoral students on the appropriate and ethical integration of GenAI in literature reviews.

Changes in plasma biomarkers differentiate clinical stages of Alzheimer's disease using immunomagnetic reduction assays.

Many groups have been using immunomagnetic reduction (IMR) for assaying plasma amyloid-β 1-40 (Aβ1-40) peptide, Aβ1-42 peptide and total tau protein (T-Tau) in cognitively normal controls (NC), patients with amnestic mild cognitive impairment (aMCI) and Alzheimer's disease dementia (ADD). Tremendous results have been independently reported. We used traditional knowledge databases (e.g., PubMed, Embase), papers published at international conferences, and private communications to obtain data involving the use of IMR assay for plasma biomarkers of plasma Aβ1-40, Aβ1-42, and T-Tau in NC, aMCI, and ADD. Results of thirty studies were analyzed, including twenty-five studies published in papers, two studies presented at conferences and three unpublished studies. Among the thirty studies, there were 1189 subjects of NC, 544 aMCI patients and 853 ADD patients. The average value of plasma Aβ1-40 in subjects significantly decreased from NC (59.72 pg/ml) to aMCI (45.92 pg/ml, p < 0.0001), which was no significant different from ADD (48.34 pg/ml, p > 0.05). The average level of plasma Aβ1-42 significantly increased from NC (15.72 pg/ml) to aMCI (17.66 pg/ml, p < 0.0001), which was not significantly different from ADD (19.39 pg/ml, p > 0.05). However, the average level of plasma T-Tau significantly increased from NC (17.92 pg/ml) to aMCI (28.26 pg/ml, p < 0.0001), further significantly increased to AD (35.51 pg/ml, p < 0.001). Plasma Aβ1-40, Aβ1-42, and T-Tau levels are able to discriminate NC from patients with aMCI and ADD. Plasma T-Tau levels are disease-severity dependent.

Inulae Flos is a potential herbal medicine to treat glioma: a study based on gene expression profile analysis, network pharmacology and molecular docking

Inulae Flos is a promising herbal medicine; however, its underlying mechanism in treating glioma remains unclear. This study aimed to elucidate the potential active ingredients and mechanisms of Inulae Flos in glioma treatment. Differentially expressed genes (DEGs) associated with gliomas were identified by analyzing the GSE186057 dataset. The bioactive components of Inulae Flos were searched in the Traditional Chinese Medicine Systems Pharmacology database, and related targets were obtained from the Swiss Target Prediction database. A protein-protein interaction (PPI) network was constructed using the STRING database. Next, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) of these common targets were analyzed using the DAVID database. Finally, AutoDock Vina was used for molecular docking analysis. The tumor-suppressive properties of the crude extract of Inulae Flos (XFH) and britanin were then evaluated with in vitro and in vivo assays. Nineteen active ingredients were obtained from Inulae Flos, and 20 DEGs were identified as the targets of Inulae Flos in glioma treatment. Britanin, luteolin and 1-O-Acetylbritannilactone were identified as the key ingredients. PIK3R1, CDK2, CCNB1, MAPK1, MAPK8, CCNA2, and AR were considered to be the hub target genes of Inulae Flos in glioma treatment. Good binding affinity was observed between the key components and hub targets. In addition, in vitro studies showed that XFH and britanin, a key component of Inulae Flos, could inhibit the growth, migration and invasion glioma cell lines U87 and U138, and repress the tumorigenesis of U138 cells in nude mice. Inulae Flos is promising for glioma treatment, and it exerts its anti-tumor activities via multiple components, targets and pathways.

BIG DATA AND STATISTICAL APPLICATIONS

In recent years, modern developments in computer and cloud technologies have led to a significant increase in the quantity and speed of generated and stored information. This increase in the amount of information has introduced a new concept into our lives called "Big Data". Big Data offers significant advantages, especially for governments and businesses, in making informed decisions about their futures. However, the magnitude and diversity of this emerging data also bring about some challenges. Traditional database systems have been inadequate in processing these data formats, necessitating the need for new tools and techniques to overcome this challenge. Today, a wide range of techniques and technologies have been developed to collect, process, analyze, and visualize “Big Data”. These techniques and technologies encompass and draw upon various fields such as statistics, computer science, applied mathematics, and economics. Keywords: big data, visualization of data, structured data, big data technologies, big data techniques.

Causal associations of tea consumption on risk of pancreatic adenocarcinoma and the mediating role of vascular endothelial growth factor D levels.

Tea is one of the most widely consumed beverages in the world. However, the association between tea and risk of pancreatic adenocarcinoma remains controversial. This study aimed to investigate the causal relationship between tea consumption and risk of pancreatic adenocarcinoma and to explore their mediating effects. The two-sample Mendelian randomisation (MR) analysis showed an inverse causal relationship between tea intake and pancreatic adenocarcinoma (OR: 0·111 (0·02, 0·85), P < 0·04). To examine the mediating effects, we explored the potential mechanisms by which tea intake reduces the risk of pancreatic adenocarcinoma. Based on the oral bioavailability and drug-like properties in Traditional Chinese Medicine Systems Pharmacology database, we selected the main active ingredients of tea. We screened out the fifteen representative targeted genes by Pharmmapper database, and the gene ontology enrichment analysis showed that these targeted genes were related to vascular endothelial growth factor (VEGF) pathway. The two-step MR analysis of results showed that only VEGF-D played a mediating role, with a mediation ratio of 0·230 (0·066, 0·394). In conclusion, the findings suggest that VEGF-D mediates the effect of tea intake on the risk of pancreatic adenocarcinoma.

Network pharmacology and experimental verification unraveled the mechanism of Bailing Capsule against asthma.

Asthma is a serious public health challenge around the world. Recent studies into traditional Chinese medicine preparations for asthma have yielded promising findings regarding Bailing Capsule's potential in bronchial asthma prevention and treatment. This study aims to initially clarify the potential mechanism of Bailing Capsule in the treatment of asthma using network pharmacology and in vitro experimental approaches. Network pharmacology was adopted to detect the active ingredients of Bailing Capsule via Traditional Chinese Medicine Systems Pharmacology Database, and the key targets and signaling pathways in the treatment of asthma were predicted. Docking and molecular dynamics simulations were conducted to verify the most important interactions formed by these probes within different regions of the binding site. The predicted targets were validated in lipopolysaccharide-induced 16HBE cell experiment. Seven active ingredients were screened from Bailing Capsule, 294 overlapping targets matched with asthma were considered potential therapeutic targets, such as SRC, TP53, STAT3, and E1A binding protein P300. The main functional pathways involving these key targets include phosphatidylinositol 3-kinase/protein kinase B, mitogen-activated protein kinase, renin-angiotensin system and other signaling pathways, which were mainly involved in the inflammatory response, apoptosis, and xenobiotic stimulus. Moreover, molecular docking showed that Cerevisterol have higher affinity for SRC, TP53, STAT3, and E1A binding protein P300 than other main active components, which is close to the docking results of the co-crystallized ligands to proteins. Consequently, Cerevisterol was selected for molecular dynamics simulation and the results show that Cerevisterol can bind most tightly to SRC, TP53, and STAT3. Bailing Capsule can promote the growth of 16HBE cell, reduce the production of IL-4, TNF-α and IL-6, and down-regulate the levels of SRC and STAT3 mRNA. This study preliminarily reveals the potential mechanism of Bailing Capsule against asthma with the aid of network pharmacology and in vitro cell experiment, which provided reference and guidance for in-depth research and clinical application.

Unlocking the potential of digital therapeutics: The need for consistent and granular inclusion in drug compendia for managed care.

The field of digital therapeutics (DTx), software programs that prevent, manage, and treat medical conditions, continues to grow. DTx offers new treatment options and has the potential to close gaps in care caused by unmet patient needs, provider shortages, or socioeconomic or geographical disparities. However, the field of DTx has not seen steady adoption owing to barriers, particularly related to coverage, payer acceptance of the category, provider use, and integration within existing health care delivery tools. One challenge for payers to effectively evaluate and cover DTx products is ensuring that consistent data elements are listed for these products in traditional drug compendia databases. Managed care organizations will need similar information about DTx product features as are available for traditional medications to inform coverage and reimbursement decisions. The Academy of Managed Care Pharmacy DTx Advisory Group developed and distributed a request for information to the 5 top drug compendia companies to assess how compendia products incorporate DTx and prescription DTx. This article summarizes how DTx are listed within different compendia products and offers insights on future data needs to adequately inform payers. As the DTx sector grows and consumer demand rises, compendia listing services will need to evolve to accommodate these new therapies and treatment modalities and facilitate patient access and efficient claims processing. Recommendations for how compendia companies can support managed care in these efforts are outlined.

Using Time-Series Databases for Energy Data Infrastructures

The management of energy market data, such as load, production, forecasts, and prices, is critical for energy market participants, who develop in-house energy data infrastructure services to aggregate data from many sources to support their business operations. Energy data management frequently involves time sensitive operations, including rapid data ingestion, real-time querying, filling in gaps from missing or delayed data, and updating large volumes of timestamped and loosely structured data, all of which demand high processing power. Traditional relational database management systems (RDBMSs) often struggle with these operations, whereas time series databases (TSDBs) appear to be a more efficient solution, providing enhanced scalability, reliability, real-time data availability and superior performance. This paper examines the advantages of TSDBs over RDBMS for energy data management, demonstrating that TSDBs can either replace or complement RDBMSs. We present quantitative improvements in digestion, integration, architecture, and performance, demonstrating that operations such as importing and querying time-series energy data, along with the overall system’s efficiency, can be significantly improved, achieving up to 100 times faster operations compared to relational databases, all without requiring extensive modifications to the existing information system’s architecture.

NoSQL Technologies in Big Data Ecosystems: a Comprehensive Review of Architectural Paradigms and Performance Metrics

The exponential growth of data in volume, velocity, and variety has challenged traditional relational database management systems, leading to the emergence and widespread adoption of NoSQL and other innovative database technologies. This article provides a comprehensive review of NoSQL databases and their role in modern Big Data infrastructure stacks. Through a systematic analysis of current literature and industry case studies, we explore the architectural paradigms, performance metrics, and use cases of various NoSQL database types, including document-based, key-value, column-family, and graph databases. Our findings indicate that NoSQL solutions offer significant advantages in scalability, flexibility, and real-time processing capabilities, particularly for unstructured and semi-structured data. However, challenges persist in areas such as data consistency, security, and interoperability with existing systems. We also examine emerging trends, including NewSQL, time-series databases, and the integration of artificial intelligence in database management. This article contributes to the understanding of how organizations can leverage NoSQL technologies to optimize their Big Data infrastructure, highlighting both the opportunities and considerations for implementation. Our conclusions underscore the transformative impact of NoSQL on data management practices and provide directions for future research in this rapidly evolving field.

Elucidating the Mechanisms of Astragalus Membranaceus in Colorectal Cancer Patients through Bioinformatics Analysis.

Astragalus membranaceus has shown positive clinical efficacy in treating colorectal cancer (CRC). This study aimed to identify the key active components of Astragalus and determine effective targets of these components in CRC patients. We identified active components of Astragalus membranaceus and differentially expressed genes in traditional Chinese medicine systems pharmacology database and The Cancer Genome Atlas. Additionally, the enrichment analysis of differential target genes (DTGs) was performed using the R-package clusterProfiler. Immunocyte correlation analysis and non-coding regulatory network construction were performed for biomarkers using Spearman's method and NetworkAnalyst. Finally, molecular docking of biomarkers and their corresponding molecule drugs was done with Autodock Vina software. We identified 20 active components of Astragalus membranaceus and 1 403 target genes through screening. A total of 2 300 differentially expressed genes, and 3 035 hub genes in CRC were screened. The integration of the target genes with the significantly differentially expressed genes and Hub genes identified resulted in a total of 86 DTGs. Subsequently, the results showed 828 enriched GO biological processes, 184 enriched GO molecular functions, 59 enriched GO cellular components, and 46 enriched KEGG pathways. We also obtained a total of 143 PPI pairs involving 67 nodes. Additionally, we constructed 45 mRNA-TF pairs, 101 miRNA-mRNA pairs, and 200 miRNA- mRNA-TF triplets. Finally, molecular docking was performed for the active component quercetin with F2 and UGT1A1 and formic acid with FGA, AHSG, and KNG1. This study identified the active components of Astragalus membranaceus and their corresponding targets in CRC. These findings provide robust evidence for precision drug therapy in patients with CRC.

A Network Pharmacological Study of Qianjin Sanhuang Decoction in the Treatment of Osteoarthritic Knee Disease

Objective: To predict the action targets of Qianjin Sanhuang decoction in the treatment of knee osteoarthritis through network pharmacology, to explore its potential multi component, multi target, multi, pathway mechanism of action, and to elaborate the relationship between the pharmacological effects of the drug and the disease from the perspective of molecular mechanism. Methods: The Chinese herbal composition of Qianjin Sanhuang decoction was determined by reading ancient texts and reviewing data, and the chemical components of Qianjin Sanhuang decoction were collected through the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform (TCMSP), and the active ingredients of the drug were obtained after oral utilization (OB) and drug-like properties (DL) screening, and the active ingredient target prediction was performed, while the active ingredient targets were retrieved from GeneCards, OMIM, and DisGeNet database to retrieve knee osteoarthritis-related targets. After taking the intersecting targets, a software called cytoscape 3.9.0 was used for mapping, with the goal of constructing a component-target network map, followed by a string database for mapping, with the goal of making a network map of target protein-target protein interactions, and finally, a gene ontology (GO) enrichment analysis and a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, this analysis process was performed in the metascape database. Results: A total of 1272 targets related to osteoarthritis of the knee were collected in this study, 89 active ingredients of Qianjin Sanhuang decoction, involving 267 targets, and 121 therapeutic targets involving osteoarthritis of the knee, and the results of GO enrichment analysis showed that it involved the response to inorganic substances, positive regulation of cell migration, response to oxygen content, response to lipopolysaccharide, response to peptides, regulation of cell adhesion, response to extracellular stimuli, negative regulation of cell differentiation, positive regulation of cell death, glandular development, regulation of apoptotic signaling pathways, development of reproductive structures, response to trauma, response to growth factors, regulation of inflammatory response, cellular response to organic recycling compounds, regulation of proteolysis, etc. After KEGG analysis, the results showed that signaling pathways such as AGE-RAGE pathway and HIF-1 pathway were involved. Conclusion: Qianjin Sanhuang decoction can improve the inflammatory response of knee osteoarthritis through potential multi-pathways, multi-targets and multi-pathways, and can have potential therapeutic effects on osteoarthritis.

Application of Network Pharmacology, Molecular Docking, and In Vitro Experimental Evaluation to Decipher the Anti-Inflammatory Mechanisms of Cirsium japonicum

Cirsium japonicum, a traditional herb, exhibits significant anti-inflammatory activity. However, the main components and potential mechanisms of C. japonicum remain unclear. The aim of this study is to investigate the anti-inflammatory mechanism of Cirsium japonicum through network pharmacology and cellular experiments. The effective components of and potential targets for the anti-inflammatory activity of C. japonicum were identified using a traditional Chinese medicine systematic pharmacology database, the TCMSP analysis platform, and the GeneCards database. The drug–component–target–disease network diagram was constructed using Cytoscape 3.8.0 software, while the protein interaction network diagram was created using the STRING database and Cytoscape 3.8.0 software. Gene ontology (GO) enrichment and KEGG pathway enrichment analysis were carried out using the DAVID database. Molecular docking between key targets and active components was constructed with AutoDock 4.2.6 software to determine the best binding target. The results revealed that 14 active components of C. japonicum targeted 171 anti-inflammatory proteins. GO function enrichment analysis yielded 173 items, while KEGG pathway enrichment analysis identified 48 signaling pathways related to inflammation regulation. Molecular docking showed a strong affinity of sitosterol, stigmasterol, and other components with key targets such as peroxisome proliferator-activated receptor α recombinant protein (PPARA) and cyclooxygenase-2 (PTGS2). Vanillin, one active ingredient of C. japonicum, inhibited the release of lipopolysaccharide (LPS)-induced inflammatory factors in RAW264.7 cells. These findings suggest that C. japonicum may exert its anti-inflammatory effects by modulating the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (Akt) signal pathway (PI3K-Akt) and apoptin signal pathway, highlighting the multi-component, multi-target, and multi-channel molecular mechanism underlying its anti-inflammatory properties. Finally, the anti-inflammatory effect of vanillin, an effective component of C. japonicum, was verified by cell experiments. This study provides a new understanding of the pharmacological mechanisms of C. japonicum in the treatment of inflammatory conditions.

Leveraging social media and community science data for environmental niche models: A case study with native Australian bees

Museum occurrence records are popular sources of information for creating Environmental Niche Models (ENMs), which allow the mapping of the potential niche ranges of species. Occurrence data is often downloaded en masse from established databases. However, the use of non-traditional data sources, such as occurrence records from community/citizen science outreach and social media, is increasing in use and abundance. Data from non-traditional data sources are potentially valuable records of information, particularly for species where museum occurrence records may be comparatively scarce. In the current study, we aimed to determine the impact of adding occurrence data from non-traditional databases to ENMs that were originally created using traditional databases with a group of comparatively understudied species, native Australian bees. We used the Maxent algorithm to model the potential environmental niches of eight species. We created three models for each species: 1) one consisting of only location data from museum specimen collection records from the Atlas of Living Australia (ALA) (a traditional database), 2) one combining ALA and geo-tagged social media (Flickr) data, and 3) a model combining ALA and geo-tagged community science data from iNaturalist. This resulted in 24 different models. By comparing the models produced from each of the augmented data sets with the traditional species data set (ALA vs. ALA & Flickr; ALA vs. ALA & iNaturalist) we showed that there were significant differences, not only in predicted ranges, but also in the weighting of environmental variables used by the models to predict the environmental niche. Differences were more greatly influenced by the geographic location of the extra occurrences rather than the number of additional occurrence points. We demonstrate the potential value and risks of including social media and community science geo-tagged image data in supplementing knowledge of species distributions, particularly for relatively under-sampled species such as native bees.

Naringenin modulates oxidative stress and lipid metabolism: Insights from network pharmacology, mendelian randomization, and molecular docking.

Previous studies have demonstrated that naringenin possesses lipid-lowering effects; however, the underlying mechanisms, particularly its specific molecular targets, remain uncertain. Using bioinformatics, three traditional Chinese medicine databases and one human disease database were integrated to establish two naringenin-target-hyperlipidemia modules: naringenin-oxidative stress (OS) and naringenin-lipid metabolism (LM). Data on 1,850 proteins from 1,871 genetic instruments were sourced from seven previous studies. Using Mendelian randomization based on data from the Integrative Epidemiology Unit genome-wide association study (case, n = 5,153; control, n = 344,069), we identified potential drug targets that were subsequently validated in the UK Biobank (396,565 individuals) and FinnGen (412,181 individuals) cohorts. Using molecular docking and molecular dynamics simulation to verify the binding ability of naringenin and causal protein. In plasma, every standard deviation increase in apolipoprotein B (APOB) was associated with an increased risk of hyperlipidemia (odds ratio [OR] = 9.37, 95% confidence interval [CI], 5.12-17.12; P = 3.58e-13; posterior probability of hypothesis 4 [PPH4] = 0.997), and the same was observed for proprotein convertase subtilisin/kexin type 9 (OR = 1.81, 95% CI, 1.51-2.16; P = 6.87e-11; PPH4 = 1) and neurocan (OR = 2.34, 95% CI, 1.82-3.01; P = 4.09e-11; PPH4 = 0.932). The intersection of two modules and Mendelian randomization result identified APOB as a key regulatory target of naringenin in the treatment of hyperlipidemia. The binding energy between naringenin and APOB was determined to be -7.7kcal/mol. Additionally, protein-protein interactions and protein-disease networks were analyzed to uncover potential connections between proteins and hyperlipidemia. This Mendelian randomization-based combined analysis offers a robust framework for elucidating the pharmacological effects of naringenin and identifying candidate proteins for further investigation in the context of hyperlipidemia treatment.

Genetic and therapeutic for oral lichen planus and diabetes mellitus: a comprehensive study

BackgroundThis study employed a bidirectional Mendelian Randomization (MR) approach to explore the causal relationships between Oral Lichen Planus (OLP), diabetes mellitus (DM), and glycemic control. It also aims to identify potential pharmacological and herbal treatments that effectively address both OLP and the metabolic dysfunctions associated with DM.MethodsThis study employs a two-way MR approach to investigate the potential causal relationships between diabetes type and glycated hemoglobin (HbA1c) levels, and the risk of OLP. We analyzed differentially expressed genes from the OLP dataset in the Genomics Expression Omnibus (GEO) database, cross-referencing these with HbA1c-related genes for enrichment analysis. Additionally, the Drug-Gene Interaction Database (DGIdb) and Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) were utilized to assess the effectiveness of specific drugs, herbs, and ingredients in treating OLP while managing blood glucose levels.ResultsThe MR analysis revealed a significant association between Type 1 Diabetes mellitus (T1DM) and an increased risk of OLP, unlike Type 2 Diabetes mellitus (T2DM). This finding indicates a unique immunological interaction in T1DM that may predispose individuals to OLP. The drug prediction analysis focused on core targets linked to OLP and HbA1c, evaluating the therapeutic potential of retinoic acid, prednisone, and thalidomide for treating OLP and regulating blood glucose levels. Additionally, herbal medicines such as Ecliptae herbaand Amygdalus communis vas, along with herbal ingredients like quercetin, luteolin, and 17-beta-estradiol, were identified for their anti-inflammatory properties and potential to mitigate metabolic dysfunction in diabetes.ConclusionThe study highlighted a complex interplay between diabetes and OLP, underscoring the efficacy of integrated therapeutic strategies that target both conditions. The findings suggest that both pharmaceutical and herbal treatments can effectively manage the clinical manifestations of OLP and associated metabolic challenges. This holistic approach to treatment could significantly enhance patient outcomes by addressing the interconnected aspects of these chronic conditions.

Construction of a blockchain based cold chain logistics information platform for Gannan navel oranges to enhance transparency and efficiency

As a high-value agricultural product, the cold chain logistics management of Gannan navel oranges has global significance. Especially in Africa and Europe, food safety and supply chain quality are critical issues. However, the existing Gannan navel orange logistics information management system relies on traditional databases and information systems, with insufficient data sharing and transparency, affecting the overall coordination of the supply chain and customer satisfaction. In addition, the traditional system is vulnerable to hacker attacks and malicious tampering of data by insiders, resulting in serious economic losses and reputation damage. So the blockchain platform constructed in this study improves the transparency and traceability of logistics data through the SMART-PBFT algorithm of the alliance chain, which optimizes the logistics management process, improves the quality of logistics services, and reduces operating costs. It improves the logistics efficiency and data transparency of local agricultural products, and also provides a demonstrative case for the cold chain management of other high-value agricultural products around the world, with broad application potential and reference value.