| Literature DB >> 36003630 |
Yun Xia1, Ren-Qi Yao2,3, Peng-Yue Zhao4, Zheng-Bo Tao5, Li-Yu Zheng2, Hui-Ting Zhou6, Yong-Ming Yao2, Xue-Min Song1.
Abstract
Introduction: As the first bibliometric analysis of COVID-19 and immune responses, this study will provide a comprehensive overview of the latest research advances. We attempt to summarize the scientific productivity and cooperation across countries and institutions using the bibliometric methodology. Meanwhile, using clustering analysis of keywords, we revealed the evolution of research hotspots and predicted future research focuses, thereby providing valuable information for the follow-up studies.Entities:
Keywords: COVID-19; SARS-CoV-2; bibliometric analysis; immune response; sepsis
Mesh:
Year: 2022 PMID: 36003630 PMCID: PMC9394856 DOI: 10.3389/fpubh.2022.939053
Source DB: PubMed Journal: Front Public Health ISSN: 2296-2565
Figure 1Detailed flowchart of search, screening, and registration on the Web of Science.
Figure 2Publication of articles in various countries/regions. (A) The total number of citations (×0.05) and H-index (×10) of articles from the top 15 countries ranked by publications' number. (B) Density map of the top 15 countries ranked by number of publications. (C) International cooperation among top 15 countries. (D) The top 15 countries were shown in chronological order of publications.
Figure 3Institutions and journals publishing articles on COVID-19 and immune response. (A) Top 15 institutions with the most publications on immunization and COVID-19. (B) The ranking list of global journals publishing the most articles on immunization and COVID-19. The X-axis represented the percentage of the published number of each institution or journal to the total number of publishments. (C) The co-authorship network of top 20 institutions, and per institution had more than 20 publications with international cooperation. (D) The top 20 institutions were shown in chronological order of publications.
Top 5 authors publishing articles on COVID-19 and immune response.
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| Alessandro Sette | USA | La Jolla Institute for Immunology | 31 | 3,633 |
| Daniela Weiskopf | USA | La Jolla Institute for Immunology | 25 | 3,106 |
| Alba Grifoni | USA | La Jolla Institute for Immunology | 24 | 3,389 |
| Marcus Buggert | Sweden | Karolinska University Hospital | 12 | 821 |
| Yun Zhang | China | J. Craig Venter Institute, La Jolla | 3 | 599 |
Lists of the top 10 most-cited articles.
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| Dysregulation of immune response in patients with coronavirus 2019 (COVID-19) in Wuhan, China ( | Tian DS | Clinical Infectious Diseases | 2020.08 | 2,450 |
| Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals ( | Sette A | Cell | 2020.06 | 1,530 |
| Reduction and functional exhaustion of T cells in patients with coronavirus disease 2019 (COVID-19) ( | Chen YW | Frontiers in Immunology | 2020.05 | 1,057 |
| Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages ( | Martin JC | Nature Reviews Immunology | 2020.06 | 947 |
| The role of cytokines including interleukin-6 in COVID-19 induced pneumonia and macrophage activation syndrome-like disease ( | Bridgewood C | Autoimmunity Reviews | 2020.06 | 776 |
| SARS-CoV-2-specific T cell immunity in cases of COVID-19 and SARS, and uninfected controls ( | Bertoletti A | Nature | 2020.08 | 748 |
| Targeting potential drivers of COVID-19: neutrophil extracellular traps ( | Egeblad M | Journal of Experimental Medicine | 2020.06 | 690 |
| Robust T-Cell Immunity in Convalescent Individuals with Asymptomatic or Mild COVID-19 ( | Buggert M | Cell | 2020.08 | 615 |
| Neutrophil extracellular traps in COVID-19 ( | Knight JS | JCI Insight | 2020.06 | 592 |
| Potent neutralizing antibodies against SARS-CoV-2 identified by high-throughput single-cell sequencing of convalescent patients' B cells ( | Xie XS | Cell | 2020.07 | 553 |
Figure 4Cluster analysis diagram of research hotspots. Keywords that appeared at least 85 times in titles and abstracts were analyzed by VOSviewer software. The larger the circle of the keyword, the more frequently it appeared. The co-occurrence times of two keywords determined the distance between them. (A) The keywords were classified into three clusters: clinical research (red), acquired immunity-related research (green), and innate immunity-related research (blue). (B) Keywords were colored in chronological order. Purple keywords were the early-emerging ones, whereas keywords in yellow appeared more recently.
Figure 5Cluster analysis plot of references for co-citation. Top 139 articles cited at least 50 times were analyzed by VOSviewer. (A) Each circle in the figure represented a reference of co-citation, and the circle size was proportional to the number of citations. All references were divided into 3 clusters according to their contents: clinical studies on COVID-19 (red), basic research on SARS-CoV-2 (blue), and the cellular and molecular basis of immune-related pathogenesis for COVID-19 disease (green). (B) The thermodynamic chart of references. All references of co-citation were colored difference according to their citations. References with the highest number of citations were marked in red.