Computer Methods in Medicine and Health Care最新文献

Network Pharmacology-Based Analysis on the Mechanism of Drug Pair of Astragalus Membranaceus and Acorus Tatarinowii in the Treatment of Alzheimer’s Disease 基于网络药理学的黄芪、石竹药物对治疗阿尔茨海默病的作用机制分析

Computer Methods in Medicine and Health Care Pub Date : 2021-11-11 DOI: 10.3233/atde210250

Xiuting Huang, Xiujin Zhang, Xiangning Li, Haozhen Wang, Chen Lu, Ziyin Lu, Xiuli Lu

{"title":"Network Pharmacology-Based Analysis on the Mechanism of Drug Pair of Astragalus Membranaceus and Acorus Tatarinowii in the Treatment of Alzheimer’s Disease","authors":"Xiuting Huang, Xiujin Zhang, Xiangning Li, Haozhen Wang, Chen Lu, Ziyin Lu, Xiuli Lu","doi":"10.3233/atde210250","DOIUrl":"https://doi.org/10.3233/atde210250","url":null,"abstract":"Alzheimer’s disease (AD) is a degenerative disease of central nervous system, which seriously threatens the life and health of the elderly people. It has been for long time that Traditional Chinese medicine (TCM) treatment for AD is effective. This study analyzed the potential target and molecular mechanism of the most often used drug pair of Astragalus membranaceus and Acorus tatarinowii to treat AD by network pharmacological method. Firstly, the method was performed to screen and sort out the active ingredients with good ADME properties and drug targets of Astragalus membranaceus and Acorus tatarinowii. Then, we searched for the disease targets related to AD, followed by the construction of the “active ingredients-target-disease” network. We implemented GO enrichment analysis and KEGG pathway enrichment analysis of related overlapped target proteins, and then constructed the “target-pathway” network diagram. Finally, the above overlapped target proteins are mapped to build a PPI high-position protein interoperability network, and we conducted the network topology analysis to screen out the core targets of Astragalus membranaceus-Acorus tatarinowii drug pair in the treatment of AD. According to network pharmacology, this research predicted the potential targets of the drug pair of Astragalus membranaceus and Acorus tatarinowii in the treatment of AD, and explored that Astragalus membranaceus-Acorus tatarinowii drug pair in the treatment of AD was the overall systematic regulating action of “multiple-ingredients, multiple-target and multiple-pathway”. It affords the reference for understanding the pathogenesis of AD and exploring new therapeutic methods and drug development in the future.","PeriodicalId":386877,"journal":{"name":"Computer Methods in Medicine and Health Care","volume":"12 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132580652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EHG-Based Preterm Delivery Prediction Algorithm Driven by Transfer Learning 迁移学习驱动下基于脑电图的早产预测算法

Computer Methods in Medicine and Health Care Pub Date : 2021-11-11 DOI: 10.3233/atde210243

Yanjun Deng, Yefei Zhang, Shenguan Wu, Lihuan Shao, Xiaohong Zhang

引用次数: 0

Cuff-Less Blood Pressure Estimation from Electrocardiogram and Photoplethysmography Based on VGG19-LSTM Network 基于VGG19-LSTM网络的心电图和光容积脉搏波无袖带血压估计

Computer Methods in Medicine and Health Care Pub Date : 2021-11-11 DOI: 10.3233/atde210241

Yanan Pu, Xiaoxue Xie, Ling Xiong, Heng Zhang

引用次数: 3

Impact Measurement of a Collaborative Pathology Network and Its Digital Support 协同病理学网络的影响测量及其数字支持

Computer Methods in Medicine and Health Care Pub Date : 2021-11-11 DOI: 10.3233/atde210240

Paolo Locatelli, F. Cirilli, Fabio Chiodini

引用次数: 0

Recognition of Child Congenital Heart Disease Using Cardiac Cycle Segment of Electrocardiogram 利用心电周期段识别儿童先天性心脏病

Computer Methods in Medicine and Health Care Pub Date : 2021-11-11 DOI: 10.3233/atde210249

Yunmei Du, Canhui Huang, Shuai Huang, Huiying Liang

{"title":"Recognition of Child Congenital Heart Disease Using Cardiac Cycle Segment of Electrocardiogram","authors":"Yunmei Du, Canhui Huang, Shuai Huang, Huiying Liang","doi":"10.3233/atde210249","DOIUrl":"https://doi.org/10.3233/atde210249","url":null,"abstract":"The results of previous studies showed that ECG could detect CHD in children with a detection rate of 76.43%. Although this result is better than the traditional CHD screening method, the sensitivity still needs to be improved if it is to be popularized clinically. Based on the previous ECG recording data, this study selects the more representative cardiac cycle segments to identify CHD, in order to achieve better screening effect. Firstly, better cardiac cycle segment data were extracted from ECG records of each patient. The final data set contains 72626 patients and each patient has a 9-lead ECG segment with duration of about one second. Then we trained a RoR network to identify the underlying patients with CHD using 62626 samples in the dataset. When tested on an independent set of 10000 patients, the network model yielded values for the sensitivity, specificity, and accuracy of 0.93, 86.3%, 85.7%, and 85.7% respectively. It can be seen that extracting more effective cardiac cycle fragments can significantly improve the sensitivity of CHD screening on the basis of ensuring better specificity, so as to find more potential patients with congenital heart disease.","PeriodicalId":386877,"journal":{"name":"Computer Methods in Medicine and Health Care","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130651906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual Features Extraction Network for Image Super-Resolution 图像超分辨率双特征提取网络

Computer Methods in Medicine and Health Care Pub Date : 2021-11-11 DOI: 10.3233/atde210239

Guosheng Zhao, Kun Wang

引用次数: 0

A Network Pharmacology Study to Explore Mechanism of the Drug Pair of Astragalus-Saposhnikoviae Radix in the Treatment of Allergic Rhinitis 网络药理学研究探讨黄芪-鼠尾草药物对治疗变应性鼻炎的作用机制

Computer Methods in Medicine and Health Care Pub Date : 2021-11-11 DOI: 10.3233/atde210242

Chen-Xin Lu, Limin Ma, Haozhen Wang, Xiuting Huang, Xiujin Zhang, Ziyin Lu, Xiuli Lu

{"title":"A Network Pharmacology Study to Explore Mechanism of the Drug Pair of Astragalus-Saposhnikoviae Radix in the Treatment of Allergic Rhinitis","authors":"Chen-Xin Lu, Limin Ma, Haozhen Wang, Xiuting Huang, Xiujin Zhang, Ziyin Lu, Xiuli Lu","doi":"10.3233/atde210242","DOIUrl":"https://doi.org/10.3233/atde210242","url":null,"abstract":"Allergic rhinitis (AR) has now become one of the major diseases affecting people’s lives, and Traditional Chinese medicine (TCM) always has good efficacy in clinical treatment. In the present study, we analyzed the most frequently used drug pair of Astragalus-Saposhnikoviae Radix (SR) in prescriptions for the treatment of allergic rhinitis by network pharmacology to reveal the modern pharmacological mechanisms of drug prevention and treatment of the disease. Firstly, the 38 active ingredients with good ADME properties from the Astragalus-SR drug pair were collected from the database, and the collated drug targets of Astragalus and SR and the targets of allergic rhinitis were mapped against each other by the network visualization software Cytoscape, followed by the establishment of a “drug active ingredient-target-disease” network diagram and the construction of a high-confidence protein-protein interaction network. Then, the common targets obtained from the disease and drug active ingredients were imported by R language for GO enrichment analysis and KEGG pathway enrichment analysis. The KEGG pathways associated with the targets of Astragalus and SR for the treatment of allergic rhinitis obtained from R enrichment analysis were imported into Cytoscape, and the CytoNCA plug-in was loaded to construct a “target-pathway” network map, and the core target wogonin (FN1) was screened. These evidences suggest that the drug pair of Astragalus-SR works in a multi-component, multi-target and integrated modulation manner for the treatment of allergic rhinitis, which provides an important basis for the treatment of allergic rhinitis.","PeriodicalId":386877,"journal":{"name":"Computer Methods in Medicine and Health Care","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122842394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Generalization of Residential Areas Based on “Paradigm” Theory and Big Data 基于“范式”理论和大数据的居住区自动概化

Computer Methods in Medicine and Health Care Pub Date : 2021-11-11 DOI: 10.3233/atde210244

Tian Duo, Peng Zhang

引用次数: 0

Application of Artificial Intelligence in the Prevention, Diagnosis and Treatment of Alzheimer’s Disease: New Hope for Dealing with Aging in China 人工智能在阿尔茨海默病防治中的应用:中国应对老龄化的新希望

Computer Methods in Medicine and Health Care Pub Date : 2021-11-11 DOI: 10.3233/atde210248

Hangtian Wang, Guofu Wang

引用次数: 0

Research on Design of Fog Computing Optimization Model for Medical Big Data 医疗大数据雾计算优化模型设计研究

Computer Methods in Medicine and Health Care Pub Date : 2021-11-11 DOI: 10.3233/atde210247

Baoling Qin

{"title":"Research on Design of Fog Computing Optimization Model for Medical Big Data","authors":"Baoling Qin","doi":"10.3233/atde210247","DOIUrl":"https://doi.org/10.3233/atde210247","url":null,"abstract":"Targeted at the current issues of communication delay, data congestion, and data redundancy in cloud computing for medical big data, a fog computing optimization model is designed, namely an intelligent front-end architecture of fog computing. It uses the network structure characteristics of fog computing and “decentralized and local” mind-sets to tackle the current medical IoT network’s narrow bandwidth, information congestion, heavy computing burden on cloud services, insufficient storage space, and poor data security and confidentiality. The model is composed of fog computing, deep learning, and big data technology. By full use of the advantages of WiFi and user mobile devices in the medical area, it can optimize the internal technology of the model, with the help of classification methods based on big data mining and deep learning algorithms based on artificial intelligence, and automatically process case diagnosis, multi-source heterogeneous data mining, and medical records. It will also improve the accuracy of medical diagnosis and the efficiency of multi-source heterogeneous data processing while reducing network delay and power consumption, ensuring patient data privacy and safety, reducing data redundancy, and reducing cloud overload. The response speed and network bandwidth of the system have been greatly optimized in the process, which improves the quality of medical information service.","PeriodicalId":386877,"journal":{"name":"Computer Methods in Medicine and Health Care","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129353119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0