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Chen S, Lan X, Yu H. A social network analysis: mental health scales used during the COVID-19 pandemic. Front Psychiatry 2023; 14:1199906. [PMID: 37706038 PMCID: PMC10495585 DOI: 10.3389/fpsyt.2023.1199906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 08/11/2023] [Indexed: 09/15/2023] Open
Abstract
Introduction The focus on psychological issues during COVID-19 has led to the development of large surveys that involve the use of mental health scales. Numerous mental health measurements are available; choosing the appropriate measurement is crucial. Methods A rule-based named entity recognition was used to recognize entities of mental health scales that occur in the articles from PubMed. The co-occurrence networks of mental health scales and Medical Subject Headings (MeSH) terms were constructed by Gephi. Results Five types of MeSH terms were filtered, including research objects, research topics, research methods, countries/regions, and factors. Seventy-eight mental health scales were discovered. Discussion The findings provide insights on the scales used most often during the pandemic, the key instruments used to measure healthcare workers' physical and mental health, the scales most often utilized for assessing maternal mental health, the tools used most commonly for assessing older adults' psychological resilience and loneliness, and new COVID-19 mental health scales. Future studies may use these findings as a guiding reference and compass.
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Affiliation(s)
| | - Xue Lan
- Department of Health Management, China Medical University, Shenyang, China
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Xu S, Fu Y, Xu D, Han S, Wu M, Ju X, Liu M, Huang DS, Guan P. Mapping Research Trends of Medications for Multidrug-Resistant Pulmonary Tuberculosis Based on the Co-Occurrence of Specific Semantic Types in the MeSH Tree: A Bibliometric and Visualization-Based Analysis of PubMed Literature (1966-2020). Drug Des Devel Ther 2023; 17:2035-2049. [PMID: 37457889 PMCID: PMC10348322 DOI: 10.2147/dddt.s409604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 06/28/2023] [Indexed: 07/18/2023] Open
Abstract
Background Before the COVID-19 pandemic, tuberculosis is the leading cause of death from a single infectious agent worldwide for the past 30 years. Progress in the control of tuberculosis has been undermined by the emergence of multidrug-resistant tuberculosis. The aim of the study is to reveal the trends of research on medications for multidrug-resistant pulmonary tuberculosis (MDR-PTB) through a novel method of bibliometrics that co-occurs specific semantic Medical Subject Headings (MeSH). Methods PubMed was used to identify the original publications related to medications for MDR-PTB. An R package for text mining of PubMed, pubMR, was adopted to extract data and construct the co-occurrence matrix-specific semantic types. Biclustering analysis of high-frequency MeSH term co-occurrence matrix was performed by gCLUTO. Scientific knowledge maps were constructed by VOSviewer to create overlay visualization and density visualization. Burst detection was performed by CiteSpace to identify the future research hotspots. Results Two hundred and eight substances (chemical, drug, protein) and 147 diseases related to MDR-PTB were extracted to form a specific semantic co-occurrence matrix. MeSH terms with frequency greater than or equal to six were selected to construct high-frequency co-occurrence matrix (42 × 20) of specific semantic types contains 42 substances and 20 diseases. Biclustering analysis divided the medications for MDR-PTB into five clusters and reflected the characteristics of drug composition. The overlay map indicated the average age gradients of 42 high-frequency drugs. Fifteen top keywords and 37 top terms with the strongest citation bursts were detected. Conclusion This study evaluated the literatures related to MDR-PTB drug therapy, providing a co-occurrence matrix model based on the specific semantic types and a new attempt for text knowledge mining. Compared with the macro knowledge structure or hot spot analysis, this method may have a wider scope of application and a more in-depth degree of analysis.
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Affiliation(s)
- Shuang Xu
- Library of China Medical University, Shenyang, Liaoning, People’s Republic of China
| | - Yi Fu
- School of Health Management, China Medical University, Shenyang, Liaoning, People’s Republic of China
| | - Dan Xu
- Library of China Medical University, Shenyang, Liaoning, People’s Republic of China
| | - Shuang Han
- Library of China Medical University, Shenyang, Liaoning, People’s Republic of China
| | - Mingzhi Wu
- Library of Shenyang Pharmaceutical University, Shenyang, Liaoning, People’s Republic of China
| | - Xinrong Ju
- Library of China Medical University, Shenyang, Liaoning, People’s Republic of China
| | - Meng Liu
- Library of China Medical University, Shenyang, Liaoning, People’s Republic of China
| | - De-Sheng Huang
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, Shenyang, Liaoning, People’s Republic of China
- Department of Intelligent Computing, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, People’s Republic of China
| | - Peng Guan
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention (China Medical University), Ministry of Education, Shenyang, Liaoning, People’s Republic of China
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang, Liaoning, People’s Republic of China
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Feng L, Wang Q, Wang J, Lin KY. A Review of Technological Forecasting from the Perspective of Complex Systems. ENTROPY 2022; 24:e24060787. [PMID: 35741508 PMCID: PMC9223049 DOI: 10.3390/e24060787] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 05/31/2022] [Accepted: 06/02/2022] [Indexed: 11/26/2022]
Abstract
Technology forecasting (TF) is an important way to address technological innovation in fast-changing market environments and enhance the competitiveness of organizations in dynamic and complex environments. However, few studies have investigated the complex process problem of how to select the most appropriate forecasts for organizational characteristics. This paper attempts to fill this research gap by reviewing the TF literature based on a complex systems perspective. We first identify four contexts (technology opportunity identification, technology assessment, technology trend and evolutionary analysis, and others) involved in the systems of TF to indicate the research boundary of the system. Secondly, the four types of agents (field of analysis, object of analysis, data source, and approach) are explored to reveal the basic elements of the systems. Finally, the visualization of the interaction between multiple agents in full context and specific contexts is realized in the form of a network. The interaction relationship network illustrates how the subjects coordinate and cooperate to realize the TF context. Accordingly, we illustrate suggest five trends for future research: (1) refinement of the context; (2) optimization and expansion of the analysis field; (3) extension of the analysis object; (4) convergence and diversification of the data source; and (5) combination and optimization of the approach.
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Affiliation(s)
- Lijie Feng
- School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China; (L.F.); (Q.W.)
- China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China
| | - Qinghua Wang
- School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China; (L.F.); (Q.W.)
| | - Jinfeng Wang
- China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China
- Correspondence:
| | - Kuo-Yi Lin
- School of Business, Guilin University of Electronic Technology, Guilin 541004, China;
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Lan X, Yu H, Cui L. Application of Telemedicine in COVID-19: A Bibliometric Analysis. Front Public Health 2022; 10:908756. [PMID: 35719666 PMCID: PMC9199898 DOI: 10.3389/fpubh.2022.908756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/03/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundTelemedicine as a tool that can reduce potential disease spread and fill a gap in healthcare has been increasingly applied during the COVID-19 pandemic. Many studies have summarized telemedicine's technologies or the diseases' applications. However, these studies were reviewed separately. There is a lack of a comprehensive overview of the telemedicine technologies, application areas, and medical service types.ObjectiveWe aimed to investigate the research direction of telemedicine at COVID-19 and to clarify what kind of telemedicine technology is used in what diseases, and what medical services are provided by telemedicine.MethodsPublications addressing telemedicine in COVID-19 were retrieved from the PubMed database. To extract bibliographic information and do a bi-clustering analysis, we used Bicomb and gCLUTO. The co-occurrence networks of diseases, technology, and healthcare services were then constructed and shown using R-studio and the Gephi tool.ResultsWe retrieved 5,224 research papers on telemedicine at COVID-19 distributed among 1460 journals. Most articles were published in the Journal of Medical Internet Research (166/5,224, 3.18%). The United States published the most articles on telemedicine. The research clusters comprised 6 clusters, which refer to mental health, mhealth, cross-infection control, and self-management of diseases. The network analysis revealed a triple relation with diseases, technologies, and health care services with 303 nodes and 5,664 edges. The entity “delivery of health care” was the node with the highest betweenness centrality at 6,787.79, followed by “remote consultation” (4,395.76) and “infection control” (3,700.50).ConclusionsThe results of this study highlight widely use of telemedicine during COVID-19. Most studies relate to the delivery of health care and mental health services. Technologies were primarily via mobile devices to deliver health care, remote consultation, control infection, and contact tracing. The study assists researchers in comprehending the knowledge structure in this sector, enabling them to discover critical topics and choose the best match for their survey work.
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Kanazawa S, Shimizu S, Kajihara S, Mukai N, Iida J, Matsuda F. Automated Recommendation of Research Keywords from PubMed That Suggest the Molecular Mechanism Associated with Biomarker Metabolites. Metabolites 2022; 12:metabo12020133. [PMID: 35208208 PMCID: PMC8875447 DOI: 10.3390/metabo12020133] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/29/2022] [Accepted: 01/29/2022] [Indexed: 12/05/2022] Open
Abstract
Metabolomics can help identify candidate biomarker metabolites whose levels are altered in response to disease development or drug administration. However, assessment of the underlying molecular mechanism is challenging considering it depends on the researcher’s knowledge. This study reports a novel method for the automated recommendation of keywords known in the literature that may be overlooked by researchers. The proposed method aided in the identification of Medical Subject Headings (MeSH) terms in PubMed using MeSH co-occurrence data. The intended users are biocurators who have identified specific biomarker metabolites from a metabolomics study and would like to identify literature-reported molecular mechanisms that are associated with both the metabolite and their research area of interest. The proposed method finds MeSH terms that co-occur with a MeSH term of the candidate biomarker metabolite as well as a MeSH term of a researcher’s known keyword, such as the name of a disease. The connectivity score S was determined using association analysis. Pilot analyses demonstrated that, while the biological significance of the obtained MeSH terms could not be guaranteed, the developed method can be useful for finding keywords to further investigate molecular mechanisms in association with candidate biomarker molecules.
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Affiliation(s)
- Shinji Kanazawa
- Shimadzu Corporation, Kyoto 604-8511, Japan; (S.K.); (S.S.); (S.K.); (N.M.); (J.I.)
- Osaka University Shimadzu Omics Innovation Research Laboratories, Osaka University, Osaka 565-0871, Japan
- Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
| | - Satoshi Shimizu
- Shimadzu Corporation, Kyoto 604-8511, Japan; (S.K.); (S.S.); (S.K.); (N.M.); (J.I.)
| | - Shigeki Kajihara
- Shimadzu Corporation, Kyoto 604-8511, Japan; (S.K.); (S.S.); (S.K.); (N.M.); (J.I.)
| | - Norio Mukai
- Shimadzu Corporation, Kyoto 604-8511, Japan; (S.K.); (S.S.); (S.K.); (N.M.); (J.I.)
| | - Junko Iida
- Shimadzu Corporation, Kyoto 604-8511, Japan; (S.K.); (S.S.); (S.K.); (N.M.); (J.I.)
- Osaka University Shimadzu Omics Innovation Research Laboratories, Osaka University, Osaka 565-0871, Japan
| | - Fumio Matsuda
- Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka 565-0871, Japan
- Correspondence: ; Tel.: +81-6-6879-7433
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Comparison of MeSH terms and KeyWords Plus terms for more accurate classification in medical research fields. A case study in cannabis research. Inf Process Manag 2021. [DOI: 10.1016/j.ipm.2021.102658] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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