A systematic study of Erzhu Erchen decoction against damp-heat internalized type 2 diabetes based on data mining and experimental verification

Background:Erzhu Erchen decoction(EZECD),which is based on Erchen decoction and enhanced with Atractylodes lancea and Atractylodes macrocephala,is widely used for the treatment of dampness and heat(The clinical manifestations of Western medicine include thirst,inability to drink more,diarrhea,yellow urine,red tongue,et al.)internalized disease.Nevertheless,the mechanism of EZECD on damp-heat internalized Type 2 diabetes(T2D)remains unknown.We employed data mining,pharmacology databases and experimental verification to study how EZECD treats damp-heat internalized T2D.Methods:The main compounds or genes of EZECD and damp-heat internalized T2D were obtained from the pharmacology databases.Succeeding,the overlapped targets of EZECD and damp-heat internalized T2D were performed by the Gene Ontology,kyoto encyclopedia of genes and genomes analysis.And the compound-disease targets-pathway network were constructed to obtain the hub compound.Moreover,the hub genes and core related pathways were mined with weighted gene co-expression network analysis based on Gene Expression Omnibus database,the capability of hub compound and genes was valid in AutoDock 1.5.7.Furthermore,and violin plot and gene set enrichment analysis were performed to explore the role of hub genes in damp-heat internalized T2D.Finally,the interactions of hub compound and genes were explored using Comparative Toxicogenomics Database and quantitative polymerase chain reaction.Results:First,herb-compounds-genes-disease network illustrated that the hub compound of EZECD for damp-heat internalized T2D could be quercetin.Consistently,the hub genes were CASP8,CCL2,and AHR according to weighted gene co-expression network analysis.Molecular docking showed that quercetin could bind with the hub genes.Further,gene set enrichment analysis and Gene Ontology represented that CASP8,or CCL2,is negatively involved in insulin secretion response to the TNF or lipopolysaccharide process,and AHR or CCL2 positively regulated lipid and atherosclerosis,and/or including NOD-like receptor signaling pathway,and TNF signaling pathway.Ultimately,the quantitative polymerase chain reaction and western blotting analysis showed that quercetin could down-regulated the mRNA and protein experssion of CASP8,CCL2,and AHR.It was consistent with the results in Comparative Toxicogenomics Database databases.Conclusion:These results demonstrated quercetin could inhibit the expression of CASP8,CCL2,AHR in damp-heat internalized T2D,which improves insulin secretion and inhibits lipid and atherosclerosis,as well as/or including NOD-like receptor signaling pathway,and TNF signaling pathway,suggesting that EZECD may be more effective to treat damp-heat internalized T2D.

Forecasting the Academic Performance by Leveraging Educational Data Mining

The study aims to recognize how efficiently Educational DataMining(EDM)integrates into Artificial Intelligence(AI)to develop skills for predicting students’performance.The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University.The first step’s initial population placements were created using Particle Swarm Optimization(PSO).Then,using adaptive feature space search,Educational Grey Wolf Optimization(EGWO)was employed to choose the optimal attribute combination.The second stage uses the SVMclassifier to forecast classification accuracy.Different classifiers were utilized to evaluate the performance of students.According to the results,it was revealed that AI could forecast the final grades of students with an accuracy rate of 97%on the test dataset.Furthermore,the present study showed that successful students could be selected by the Decision Tree model with an efficiency rate of 87.50%and could be categorized as having equal information ratio gain after the semester.While the random forest provided an accuracy of 28%.These findings indicate the higher accuracy rate in the results when these models were implemented on the data set which provides significantly accurate results as compared to a linear regression model with accuracy(12%).The study concluded that the methodology used in this study can prove to be helpful for students and teachers in upgrading academic performance,reducing chances of failure,and taking appropriate steps at the right time to raise the standards of education.The study also motivates academics to assess and discover EDM at several other universities.

Integrated data mining and network pharmacology to discover a novel traditional Chinese medicine prescription against diabetic retinopathy and reveal its mechanism

Background:Diabetic retinopathy(DR)is currently the leading cause of blindness in elderly individuals with diabetes.Traditional Chinese medicine(TCM)prescriptions have shown remarkable effectiveness for treating DR.This study aimed to screen a novel TCM prescription against DR from patents and elucidate its medication rule and molecular mechanism using data mining,network pharmacology,molecular docking and molecular dynamics(MD)simulation.Method:TCM prescriptions for treating DR was collected from patents and a novel TCM prescription was identified using data mining.Subsequently,the mechanism of the novel TCM prescription against DR was explored by constructing a network of core TCMs-core active ingredients-core targets-core pathways.Finally,molecular docking and MD simulation were employed to validate the findings from network pharmacology.Result:The TCMs of the collected prescriptions primarily possessed bitter and cold properties with heat-clearing and supplementing effects,attributed to the liver,lung and kidney channels.Notably,a novel TCM prescription for treating DR was identified,composed of Lycii Fructus,Chrysanthemi Flos,Astragali Radix and Angelicae Sinensis Radix.Twenty core active ingredients and ten core targets of the novel TCM prescription for treating DR were screened.Moreover,the novel TCM prescription played a crucial role for treating DR by inhibiting inflammatory response,oxidative stress,retinal pigment epithelium cell apoptosis and retinal neovascularization through various pathways,such as the AGE-RAGE signaling pathway in diabetic complications and the MAPK signaling pathway.Finally,molecular docking and MD simulation demonstrated that almost all core active ingredients exhibited satisfactory binding energies to core targets.Conclusions:This study identified a novel TCM prescription and unveiled its multi-component,multi-target and multi-pathway characteristics for treating DR.These findings provide a scientific basis and novel insights into the development of drugs for DR prevention and treatment.

Detection of Knowledge on Social Media Using Data Mining Techniques

In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), such as extracting information related to people’s behaviors and interactions to analyze feelings or understand the behavior of users or groups, and many others. This extracted knowledge has a very important role in decision-making, creating and improving marketing objectives and competitive advantage, monitoring events, whether political or economic, and development in all fields. Therefore, to extract this knowledge, we need to analyze the vast amount of data found within social media using the most popular data mining techniques and applications related to social media sites.

Aviation Safety and Data Mining in Marketing Dimension

The aviation industry is a sector that is developing, changing and growing every day in terms of technological and legal framework. There are generally three factors that enable airlines to hold on to the market. These factors are safety, service quality and price. Airline companies can analyze the customers in the market with a focus on price and quality and develop a business model according to their expectations. For example, business class and economy class passenger expectations are different from each other, so the service and price to be offered to them will be different. However, all customers have one common expectation and that is safety. No matter how high quality the service is or how cheap the price is, no one wants to fly with an airline or plane that is not safe. From an airline company’s point of view, an accident or breakdown of one of the company’s aircraft can cause irreparable image loss and financial damage. If we look at past examples, we see that there are many airline companies or maintenance organizations that could not recover after an accident and went bankrupt. Safety is an indispensable factor. Therefore, there is a unit in the sector called the safety management system (SMS), which collects data by taking a proactive and reactive approach. The way and purpose of the safety management system is to take a proactive approach to recognize and prevent unsafe situations before they cause accidents or breakdowns, or to take a reactive approach to find the causes of accidents and breakdowns that have occurred as a result of certain factors and to take the necessary measures to prevent the same situations from happening again in the sector. The field of data mining, which is necessary to predict the future behavior of customers in the field of marketing, is an area that marketing also values. In this study, data mining studies to ensure safety in the aviation industry and the security of customer information in marketing will be emphasized, firstly, the concept and importance of data mining will be mentioned.

Construction and Practice of Teaching Evaluation System Based on Data Mining

The teaching quality evaluation system based on data mining technology can accurately and fairly identify the core driving factors to improve teaching quality.This method adopts the analysis of big data correlation rules,including data collection and processing preparation steps,builds the data warehouse of association rules,and then generates an educational quality evaluation framework using the principle of data mining.Based on this,this paper analyzes the construction design and method of the teaching evaluation system under data mining,hoping to provide help for the improvement of the teaching evaluation system and the improvement of teaching quality.

Exploring the medication pattern and mechanism of action of traditional Chinese medicine in treating polycystic ovary syndrome with kidney deficiency and blood stasis based on data mining and network pharmacology

Background:Using network pharmacology to explore the potential molecular mechanism of traditional Chinese medicine in treating polycystic ovary syndrome(PCOS)with kidney deficiency and blood stasis syndrome.Method:Collect the related literature materials of PCOS with kidney deficiency and blood stasis syndrome treated by traditional Chinese medicine in four databases in recent ten years,extract the information of prescriptions and complete the frequency analysis.Traditional Chinese Medicine Systems Pharmacology Database was used to screen out the effective components.Use Online Mendelian Inheritance in Man and other databases to screen PCOS disease targets.The intersection targets obtained by clustering prescription and PCOS disease targets were submitted to STRING database for protein-protein interaction network analysis,and Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes pathways were analysed by Metascape.Result:There are 155 kinds of traditional Chinese medicines used in the literature.The most commonly utilized ones are Cuscutae Semen,Angelicae Sinensis Radix,and Rehmanniae Radix Praeparata.The results of the cluster analysis indicated that the plants most commonly found throughout the prescription were Leonuri Herba,Lycopi Herba,Dipsaci Radix,etc.GO results show that biological processes include cell reaction to organic nitrogen compounds and cell reaction to nitrogen compounds.The functional display of GO molecule includes cytokine receptor binding,signal receptor regulator activity and so on.Kyoto Encyclopedia of Genes and Genomes results show that the possible mechanisms of action are cancer pathway,an endocrine resistance signal pathway.Conclusion:Through data mining,the cluster prescription for PCOS with kidney deficiency and blood stasis syndrome is Leonuri Herba,Lycopi Herba,Dipsaci Radix,etc.The network pharmacology research of cluster prescription shows that the main drug components for treating PCOS with kidney deficiency and blood stasis syndrome are quercetin,kaempferol,luteolin,tanshinone IIA,etc.,which act on PTGS2,NCOA2,and other targets,and treat PCOS with kidney deficiency and blood stasis syndrome through cancer and endocrine resistance.

Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so on.Statistical data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the data.It varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other resolves.Thus,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)model.In the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation complexity.For rainfall prediction.Convolution neural network with long short-term memory(CNN-LSTM)technique is exploited.At last,this study involves the pelican optimization algorithm(POA)as a hyperparameter optimizer.The experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive class.The comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

Integrative multi-omics and systems bioinformatics in translational neuroscience:A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information,with its application to neuroscience termed neuroinformatics.Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms,which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases.Importantly,integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile.In this review,we first summarize data mining studies utilizing datasets from the individual type of omics analysis,including epigenetics/epigenomics,transcriptomics,proteomics,metabolomics,lipidomics,and spatial omics,pertaining to Alzheimer's disease,Parkinson's disease,and multiple sclerosis.We then discuss multi-omics integration approaches,including independent biological integration and unsupervised integration methods,for more intuitive and informative interpretation of the biological data obtained across different omics layers.We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks.Finally,we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery,therapeutic development,and elucidation of disease mechanisms.We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Correlation knowledge extraction based on data mining for distribution network planning

Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.

Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation

Imagine numerous clients,each with personal data;individual inputs are severely corrupt,and a server only concerns the collective,statistically essential facets of this data.In several data mining methods,privacy has become highly critical.As a result,various privacy-preserving data analysis technologies have emerged.Hence,we use the randomization process to reconstruct composite data attributes accurately.Also,we use privacy measures to estimate how much deception is required to guarantee privacy.There are several viable privacy protections;however,determining which one is the best is still a work in progress.This paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data results.Further-more,this paper investigates the use of arbitrary nature with perturbations in privacy preservation.According to the research,arbitrary objects(most notably random matrices)have"predicted"frequency patterns.It shows how to recover crucial information from a sample damaged by a random number using an arbi-trary lattice spectral selection strategy.Thisfiltration system's conceptual frame-work posits,and extensive practicalfindings indicate that sparse data distortions preserve relatively modest privacy protection in various situations.As a result,the research framework is efficient and effective in maintaining data privacy and security.

Study on prescription medication mode and mechanism of traditional Chinese medicine in the treatment of noncritical COVID-19 based on data mining

Background:As of 2023,coronavirus disease 2019(COVID-19)is still spreading globally.Therefore,we aim to integrate non-critical COVID-19 high-frequency and high-targeting Chinese medicines to provide a reference for clinical prescriptions to improve COVID-19-related symptoms.Materials and methods:The information on non-critical COVID-19 high-frequency Chinese medicines in the diagnosis and treatment of COVID-19 was obtained by the TCM inheritance support platform.Using network pharmacology and molecular docking technology,high-targeting Chinese medicines with good docking activity with COVID-19 receptors angiotensin-converting enzyme-II(ACE2),3CLpro and tyrosine-protein kinase receptor UFO(AXL)were obtained.A new prescription for non-critical COVID-19 was established by integrating high-frequency and high-targeting Chinese medicines.Rats with acute lung injury induced by lipopolysaccharide were used as the experimental model.The histopathological changes in the lungs of rats in each group were observed by hematoxylin-eosin staining.The lung coefficient of rats was measured.The levels of IL-6,TNF-α,and IL-1βin serum were detected by enzyme-linked immunosorbent assay.The mRNA and protein levels of ACE2 and AXL in lung tissue were detected by real-time quantitative polymerase chain reaction and western blot.Results:Through data mining,it was found that there were 39 high-frequency traditional Chinese medicines for non-critical COVID-19 in the diagnosis and treatment guidelines.According to network pharmacology and molecular docking,30 highly targeted traditional Chinese drugs for COVID-19 were found.The new prescriptions for non-critical COVID-19 were comprehensively obtained,including Glycyrrhizae Radix,Ephedra Herba,Amygdalus Communis Vas,Gypsum Fibrosum,Descurainiae Semen,Atractylodes Lancea,Scutellariae Radix,Amomum Tsao-Ko Crevostet,Forsythiae Fructus,Pogostemon cablin,Magnolia Officinalis.Compared with the LPS-induced lung injury model group,the medium dose of the new prescription group had significantly alleviated pathological changes in lung tissue,decreased lung coefficient,decreased contents of IL-6,TNF-αand IL-1β,and increased mRNA and protein expression of ACE2 and AXL(P<0.05).Conclusion:Based on data mining,network pharmacology and molecular docking technology,the new prescription for non-critical COVID-19 established by this method has an anti-inflammatory effect on rats with acute lung injury induced by lipopolysaccharide and can provide a reference for clinicians to alleviate the symptoms related to non-critical COVID-19.

Linking Competitors’ Knowledge and Developing Innovative Products Using Data Mining Techniques

In this article, the relationship between the knowledge of competitors and the development of new products in the field of capital medical equipment has been investigated. In order to identify the criteria for measuring competitors’ knowledge and developing new capital medical equipment products, marketing experts were interviewed and then a researcher-made questionnaire was compiled and distributed among the statistical sample of the research. Also, in order to achieve the goals of the research, a questionnaire among 100 members of the statistical community was selected, distributed and collected. To analyze the gathered data, the structural equation modeling (SEM) method was used in the SMART PLS 2 software to estimate the model and then the K-MEAN approach was used to cluster the capital medical equipment market based on the knowledge of actual and potential competitors. The results have shown that the knowledge of potential and actual competitors has a positive and significant effect on the development of new products in the capital medical equipment market. From the point of view of the knowledge of actual competitors, the market of “MRI”, “Ultrasound” and “SPECT” is grouped in the low knowledge cluster;“Pet MRI”, “CT Scan”, “Mammography”, “Radiography, Fluoroscopy and CRM”, “Pet CT”, “SPECT CT” and “Gamma Camera” markets are clustered in the medium knowledge. Finally, “Angiography” and “CBCT” markets are located in the knowledge cluster. From the perspective of knowledge of potential competitors, the market of “angiography”, “mammography”, “SPECT” and “SPECT CT” in the low knowledge cluster, “CT scan”, “radiography, fluoroscopy and CRM”, “pet CT”, “CBCT” markets in the medium knowledge cluster and “MRI”, “pet MRI”, “ultrasound” and “gamma camera” markets in the high knowledge cluster are located.

Development of a Data Mining Model to Detect Cardiovascular Disease

In the previous publication on Volume 15 No 9, September 30, 2022 of IJCN, we analyzed “Data Mining as a Technique for Healthcare Approach”. In this edition, emphasis has been made on the “Development of Data Mining Model to Detect Cardiovascular Diseases (CVD)”. A Software was developed using the internationally accepted Software Engineering Methodology (SSADM), coding by OOP and packing by prototyping methodologies. Among others, this paper discusses;Cardiovascular diseases, Data Mining Algorithm, Analysis and Information flow of the Present System, Data flow and High level flow of the Proposed System, Modulating, System Design and Development, Hardware and Software Specifications, System Testing, Evaluation and Documentation.

Study on Yan-Xin Wang’s medication experience and regularity in treating Insomnia from the liver based on data mining

Background:Professor Yan-Xin Wang has been committed to the use of traditional Chinese medicine formulas to treat insomnia from the liver for many years,and has achieved excellent clinical results.In order to better inherit Yan-Xin Wang’s academic thoughts.The purpose of this study is to use clinical data to explore the clinical experience of Prof.Yan-Xin Wang in the application of Chinese medicine to treat insomnia patients from the liver,explore the compatibility and medication rules of traditional Chinese medicine,and give more clinical treatment ideas for insomnia.Methods:The general data and prescription information of insomnia patients treated with Chinese herbal medicine by Prof.Yan-Xin Wang from January 1,2021,to December 31,2021,were summarized according to the inclusion and exclusion criteria,and the data were subjected to frequency statistics and drug association rules,complex network diagram analysis and cluster analysis.Results:A total of 159 patients and prescriptions were included in the study,of which 81.1%were women and 18.9%were men,containing 128 herbs;the highest frequency of use was 91.8%for Bupleuri Radix.Six Chinese herbs were used more than 70%of the time,namely Bupleuri Radix,Scutellariae Radix,oyster shell,Glycyrrhizae Radix,Os Draconis,and Ziziphi Spinosae Semen.The top 20 herbs in terms of frequency of use were analyzed in terms of the four Qi,five flavours,and their attributions.The four Qi were mainly calm and warm,the five flavours were mainly bitter and acrid,followed by sweet,and the attributions were mainly to the liver,spleen,and heart meridians.The Chinese medicine association rules set the confidence level>80%and the support level>10%,resulting in 10 two-herb and three-herb associations with the highest confidence level,such as Os Draconis is associated with oyster shell,Platycodonis Radix is associated with Achyranthis Bidentatae Radix,Scutellariae Radix is associated with Bupleuri Radix,Os Draconis,Bupleuri Radix is associated with oyster shell,Os Draconis,Scutellariae Radix is associated with oyster shell,etc.Cluster analysis yielded 3 classes of drug formulas.The complex network diagram shows that the core prescription drugs are composed of Bupleuri Radix,Chuanxiong Rhizoma,Pseudostellariae Radix,Jujubae Fructus,Os Draconis,Coptidis Rhizoma,Scutellariae Radix,Ziziphi Spinosae Semen,Smilacis Glabrae Rhizoma,Cinnamomi Cortex,White Moutan Cortex,Atractylodis Rhizoma,Glycyrrhizae Radix,oyster shell,Rehmanniae Radix Praeparata,Tritici Levis Fructus,Pinelliae Rhizoma Praeparatum,and Cinnamomi Ramulus.Conclusion:Prof.Yan-Xin Wang believes that the main treatment for patients with insomnia is based on the liver,by tonifying the deficiency and supporting the righteousness,mutually regulating the liver and spleen,and calming the mind and nourishing the heart,while adding and subtracting appropriate herbs according to the patient’s co-morbidities,which can significantly improve the patient’s insomnia symptoms.

Analysis of Tourism Demand Difference Based on Data Mining and Intelligent Analysis

To serve as a reference for future foreign tourism study,relevant tourist sectors have done in-depth investigations on foreign tourism both domestically and internationally.A study of outbound tourism activities from the viewpoint of tourists can examine its development law and create successful marketing tactics based on the rise in the number of foreign tourists.Based on this,this study suggests a data mining technique to examine the variations in travel needs and marketing tactics among various consumer groups.The combined example analysis demonstrates how logical and useful our data mining analysis is.Our data tests demonstrate that the tourism strategy outlined in this paper can enhance the number of tourists by piquing their interest based on the rise in the number of international travellers travelling overseas.

Exploring medication rules in Chinese randomized controlled trials of type 2 diabetes based on data mining technology

Background:To systematically summarize and categorize the Chinese herbal medicine in the domestic traditional Chinese medicine(TCM)literature on type 2 diabetes mellitus(T2DM),in this paper,we mine traditional Chinese medicine data for relationships and provide for future practitioners and researchers.Methods:Taking randomized controlled trials on the treatment of T2DM in TCM as the research theme,we searched for full-text literature in three major clinical databases,including CNKI,Wan Fang,and VIP,published between 1990 and 2020.We then conducted frequency statistics,cluster analysis,association rules extraction,and principal component analysis based on a corpus of medical academic words extracted from 1116 research articles.Results:The most frequently used is Astragali Radix,and the most commonly used two-herb combination in T2DM treatment consisted of Coptidis Rhizoma and Moutan Cortex.Moutan Cortex,Alismatis Rhizoma,and Dioscoreae Rhizoma were the most frequently used three-herb combination.We found a“lung”and“liver”and“kidney”model and confirmed the value of classical meridian tropism theory and pattern identification.The treatment is mainly to fill deficiency and clear heat and consider water infiltration,dampness,blood circulation,and silt.Conclusion:This study provides an in-depth perspective on the TCM medication rules for T2DM and offers practitioners and researchers valuable information about the current status and frontier trends of TCM research on T2DM in terms of diagnosis and treatment.

A retrospective study on professor Gui-Qi Xuan’s experience in traditional Chinese medicine treatment on attention-deficit hyperactivity disorder: based on data mining and network pharmacology

Background:To investigate the clinical medication approach of Professor Guiqi Xuan(Prof.Xuan)in treating pediatric patients with attention-deficit hyperactivity disorder(ADHD)and the potential mechanism of the core herbal prescription.Methods:Following medical record information pretreatment,the Traditional Chinese Medicine(TCM)inheritance computing platform system V3.0 was utilized to analyze the standardized data.The associate rules were summarized to identify the core prescription for treating ADHD.The extracted core herbal prescription’s active compounds and potential targets were used to establish a protein-protein interaction network of active ingredient-disease targets.Cytoscape 3.9.1 software was used to analyze the network’s topological parameters to obtain the key active ingredients and their targets.The Bioconductor data package of R4.0.2 was used to analyze the gene ontology biological functions and Kyoto Encyclopedia of Genes and Genomes pathways of key targets.Results:Two hundred and twenty-seven entries derived from TCM record information were selected.Through data mining,it was found that 62.5%of pediatric patients had short-tempered behavior,nearly half had sleep problems,and 30%-40%had picky eating and polyphagia issues.The highest-frequency syndrome type was kidney deficiency and liver hyperactivity.Deficiency,fire,phlegm,and dyspeptic food were the main pathological factors for ADHD.Prof.Xuan’s treatment of ADHD mainly focused on replenishing kidney essence and subduing Yang(active,external,ascending,warm,bright,functional and excited pertain to Yang).The core herbal prescription for ADHD included Yuan-zhi,Yi-zhi,Gui-jia,Bai-shao,Long-chi,Ci-shi,Shi-chang-pu,Yu-jin,Fu-shen,and Huang-jing.The protein-protein interaction network showed that MAOA,ADRB2,FOS,MAOB,and SLC6A3 were the five key targets essential in treating ADHD with core herbal prescriptions.The gene ontology biological function of crucial targets mainly involved G protein-coupled amine receptor activity,catecholamine binding,and neurotransmitter transmembrane transporter activity.Analysis of Kyoto Encyclopedia of Genes and Genomes pathways showed that the dopaminergic synapse signaling and neuroactive ligand-receptor interaction pathways were significantly enriched and may be the primary routes for the main treatment of ADHD.Conclusion:Prof.Xuan’s treatment of ADHD has achieved satisfactory clinical effects by supplementing the kidney,replenishing the essence,opening the orifices,nourishing the Yin(static,internal,descending,cold,dim,organic,depressed and pertain to Yin),and subduing the Yang.The major prescription predominantly affects catecholamine binding,neuroactive ligand-receptor interaction,G protein-coupled amine receptor function,and signaling pathways for dopaminergic synapses.Our findings showed that the methodology and software used in this research could explore and analyze the mechanism behind Prof.Xuan’s clinical medication rule for treating ADHD in children.

Identifying medication regularity of traditional Chinese medicine and potential pharmacological mechanism of Jiedu Sangen decoction in colorectal cancer treatment by data mining and network pharmacology

Objective:Colorectal cancer(CRC)is one of the most common malignancies in the world,and traditional Chinese medicine(TCM)is widely used in its treatment in China.However,the medication rules of TCM for CRC treatment remain unclear.Therefore,data mining combined with network pharmacology was utilized to establish treatment principles and rules.Methods:The CRC cases treated at Zhejiang Provincial Hospital of Chinese Medicine from January 1,2016 to October 31,2021 were analyzed using data mining methods.UPLC-Q/TOF-MS analysis was performed to identify the chemical composition of Jiedu Sangen decoction(JSD).Network pharmacology was used to reveal the therapeutic mechanism of JSD.Results:A total of 312 cases 2,998 prescriptions that met the inclusion criteria used 343 kinds of traditional Chinese medicines.The nature of the herbs used in treatment was mainly warm and mild.The taste was mainly sweet,bitter,and pungent.The meridian tropisms were mainly the spleen meridian,followed by lung and stomach meridians.Tonifying and replenishing herbs were the most frequently used in treatment.High-frequency herbs were classified into 11 categories by cluster analysis,and 42 association rules were obtained by association rule analysis.Combined with complex network analysis,3 core prescriptions for clinical CRC treatment were obtained.Jiedu Sangen decoction contains 64 chemical ingredients,out of which 31 active ingredients were identified,including polydatin,caffeic acid,and glutamic acid,along with 130 potential targets such as AKT1,SRC,and MAPK1.Jiedu Sangen decoction may play a role in regulating inflammation,immunity,metabolism,and hormones in the development of CRC via pathways such as the relaxin signaling pathway,IL-17 signaling pathway,prolactin signaling pathway,and T cell receptor signaling pathway.Conclusions:This study summarizes the treatment and medication principles for clinical CRC treatment,promoting the inheritance and development of the traditional Chinese medical experience.

Medication Rules of Traditional Chinese Medicine for Treating Threatened Abortion in the Early Stage of Pregnancy with Subchorionic Haematoma Based on Data Mining

Objective:Using data mining technology to explore the rules of traditional Chinese medicine(TCM)in the treatment of threatened abortion in the early stage of pregnancy with sub-chorionic haematoma(SCH).Methods:Literature of TCM in the treatment of threatened abortion in the early stage of pregnancy with SCH were retrieved from CNKI,VIP,WANFANG and Pubmed,EMBASE.The literature information database was established to be used for descriptive analysis,association rule analysis and cluster analysis of relevant data.Results:A total of 100 literatures were included,involving 114 Chinese herbs.The efficacy of Chinese herbs were mainly tonic drugs,hemostatic drugs,heat-clearing drugs,dissolving blood stasis and hemostatic drugs.The medicinal properties were mostly mild and warm,and the taste of the drug was mainly sweet,bitter and pungent.The liver meridian,spleen meridian and kidney meridian were frequently used.The commonly used drug pair combination was"Xu duan(Radix dipsaci,续断)-Tusizi(Semen Cuscutae,菟丝子)",and the core combination was"Tusizi-Xu duan-Ejiao(Donkeyhide gelatin,阿胶)-Baizhu(Atractylodes macrocephala,白术)-Dangshen(Codonopsis pilosula,党参)".Commonly used drugs for removing blood stasis and hemostasis were with Sanqi(Panax notoginseng,三七),Puhuang(cattail pollen,蒲黄),and Qiancao(Radix Rubiae,茜草).Conclusion:Data mining traditional Chinese medicine for the treatment of threatened abortion in the early stage of pregnancy with SCH clinically commonly used drug efficacy,taste,meridian,commonly used drug pairs,core combination and commonly used blood stasis hemostatic drugs,has important reference significance for the treatment of threatened abortion in the early stage of pregnancy combined with SCH.