Visual Analysis of International Environmental Security Management Research (1997-2021) Based on VOSviewer and CiteSpace

Machine learning-based myocardial infarction bibliometric analysis

Purpose

This study analyzed the research trends in machine learning (ML) pertaining to myocardial infarction (MI) from 2008 to 2024, aiming to identify emerging trends and hotspots in the field, providing insights into the future directions of research and development in ML for MI. Additionally, it compared the contributions of various countries, authors, and agencies to the field of ML research focused on MI.

Method

A total of 1,036 publications were collected from the Web of Science Core Collection database. CiteSpace 6.3.R1, Bibliometrix, and VOSviewer were utilized to analyze bibliometric characteristics, determining the number of publications, countries, institutions, authors, keywords, and cited authors, documents, and journals in popular scientific fields. CiteSpace was used for temporal trend analysis, Bibliometrix for quantitative country and institutional analysis, and VOSviewer for visualization of collaboration networks.

Results

Since the emergence of research literature on medical imaging and machine learning (ML) in 2008, interest in this field has grown rapidly, particularly since the pivotal moment in 2016. The ML and MI domains, represented by China and the United States, have experienced swift development in research after 2015, albeit with the United States significantly outperforming China in research quality (as evidenced by the higher impact factors of journals and citation counts of publications from the United States). Institutional collaborations have formed, notably between Harvard Medical School in the United States and Capital Medical University in China, highlighting the need for enhanced cooperation among domestic and international institutions. In the realm of MI and ML research, cooperative teams led by figures such as Dey, Damini, and Berman, Daniel S. in the United States have emerged, indicating that Chinese scholars should strengthen their collaborations and focus on both qualitative and quantitative development. The overall direction of MI and ML research trends toward Medicine, Medical Sciences, Molecular Biology, and Genetics. In particular, publications in "Circulation" and "Computers in Biology and Medicine" from the United States hold prominent positions in this study.

Conclusion

This paper presents a comprehensive exploration of the research hotspots, trends, and future directions in the field of MI and ML over the past two decades. The analysis reveals that deep learning is an emerging research direction in MI, with neural networks playing a crucial role in early diagnosis, risk assessment, and rehabilitation therapy.

Moonlighting Proteins of Human and Some Other Eukaryotes. Evolutionary Aspects

This review presents materials on formation of the concept of moonlighting proteins and general characteristics of different similar proteins. It is noted that the concept under consideration is based on the data on the existence in different organisms of individual genes, protein products of which have not one, but at least two fundamentally different functions, for example, depending on cellular or extracellular location. An important feature of these proteins is that their functions can be switched. As a result, in different cellular compartments or outside the cells, as well as under a number of other circumstances, one of the possible functions can be carried out, and under other conditions, another. It is emphasized that the significant interest in moonlighting proteins is due to the fact that information is currently accumulating about their involvement in many vital molecular processes (glycolysis, translation, transcription, replication, etc.). Alternative hypotheses on the evolutionary origin of moonlighting proteins are discussed.

Analyzing shifts in age-related macular degeneration research trends since 2014: A bibliometric study with triple-map Sankey diagrams (TMSD)

BACKGROUND

Age-related macular degeneration (AMD) is the primary cause of vision impairment in older adults, especially in developed countries. While many articles on AMD exist in the literature, none specifically delve into the trends based on document categories. While bibliometric studies typically use dual-map overlays to highlight new trends, these can become congested and unclear with standard formats (e.g., in CiteSpace software). In this study, we introduce a unique triple-map Sankey diagram (TMSD) to assess the evolution of AMD research. Our objective is to understand the nuances of AMD articles and show the effectiveness of TMSD in determining whether AMD research trends have shifted over the past decade.

METHODS

We collected 7465 articles and review pieces related to AMD written by ophthalmologists from the Web of Science core collection, accumulating article metadata from 2014 onward. To delve into the characteristics of these AMD articles, we employed various visualization methods, with a special focus on TMSD to track research evolution. We adopted the descriptive, diagnostic, predictive, and prescriptive analytics (DDPP) model, complemented by the follower-leading clustering algorithm (FLCA) for clustering analysis. This synergistic approach proved efficient in identifying and showcasing research focal points and budding trends using network charts within the DDPP framework.

RESULTS

Our findings indicate that: in countries, institutes, years, authors, and journals, the dominant entities were the United States, the University of Bonn in Germany, the year 2021, Dr Jae Hui Kim from South Korea, and the journal "Retina"; in accordance with the TMSD, AMD research trends have not changed significantly since 2014, as the top 4 categories for 3 citing, active, and cited articles have not changed, in sequence (Ophthalmology, Science & Technology - Other Topics, General & Internal Medicine, Pharmacology & Pharmacy).

CONCLUSION

The introduced TMSD, which incorporates the FLCA algorithm and features in 3 columns-cited, active, and citing research categories-offers readers clearer insights into research developments compared to the traditional dual-map overlays from CiteSpace software. Such tools are especially valuable for streamlining the visualization of the intricate data often seen in bibliometric studies.

Global trends in COVID-19 Alzheimer's related research: a bibliometric analysis

Background

The COVID-19 pandemic has significantly impacted public health, putting people with Alzheimer's disease at significant risk. This study used bibliometric analysis method to conduct in-depth research on the relationship between COVID-19 and Alzheimer's disease, as well as to predict its development trends.

Methods

The Web of Science Core Collection was searched for relevant literature on Alzheimer's and Coronavirus-19 during 2019-2023. We used a search query string in our advanced search. Using Microsoft Excel 2021 and VOSviewer software, a statistical analysis of primary high-yield authors, research institutions, countries, and journals was performed. Knowledge networks, collaboration maps, hotspots, and regional trends were analyzed using VOSviewer and CiteSpace.

Results

During 2020-2023, 866 academic studies were published in international journals. United States, Italy, and the United Kingdom rank top three in the survey; in terms of productivity, the top three schools were Harvard Medical School, the University of Padua, and the University of Oxford; Bonanni, Laura, from Gabriele d'Annunzio University (Italy), Tedeschi, Gioacchino from the University of Campania Luigi Vanvitelli (Italy), Vanacore, Nicola from Natl Ctr Dis Prevent and Health Promot (Italy), Reddy, P. Hemachandra from Texas Tech University (USA), and El Haj, Mohamad from University of Nantes (France) were the authors who published the most articles; The Journal of Alzheimer's Disease is the journals with the most published articles; "COVID-19," "Alzheimer's disease," "neurodegenerative diseases," "cognitive impairment," "neuroinflammation," "quality of life," and "neurological complications" have been the focus of attention in the last 3 years.

Conclusion

The disease caused by the COVID-19 virus infection related to Alzheimer's disease has attracted significant attention worldwide. The major hot topics in 2020 were: "Alzheimer' disease," COVID-19," risk factors," care," and "Parkinson's disease." During the 2 years 2021 and 2022, researchers were also interested in "neurodegenerative diseases," "cognitive impairment," and "quality of life," which require further investigation.