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. 2024 Apr 26;10(10):e30315.
doi: 10.1016/j.heliyon.2024.e30315. eCollection 2024 May 30.

Knowledge mapping of links between dendritic cells and allergic diseases: A bibliometric analysis (2004-2023)

Affiliations

Knowledge mapping of links between dendritic cells and allergic diseases: A bibliometric analysis (2004-2023)

Xianghe Meng et al. Heliyon. .

Abstract

In this study, bibliometric analysis was carried out to comprehend the global research trends, hotspots, scientific frontiers, and output characteristics of the links between dendritic cells (DCs) and allergic diseases from 2004 to 2023. Publications and their recorded information were retrieved from the Web of Science Core Collection (WoSCC). VOSviewer and Citespace were used to visualize the hotspots and trends of research area. ChemBio 3D, Autodock tools, and Discovery Studio were used to visualize the molecular docking results of hotspots. A total of 4861 articles were retrieved. The number of publications (Np) was in a high and stable state. Years 2011 and 2017 were two peaks in Np. The largest contributor in terms of publications, scholars, and affiliations was the USA. The paper published in NATURE MEDICINE (IF: 82.9) and written by Trompette, A in 2006 had the highest global citation score (GCS). Keywords, such as "asthma," "t-cells," "inflammation," "expression," "atopic dermatitis," "food allergy," "gut microbiota," "murine model," and "cytokines related to immunity" appeared the most frequently. Most of the binding free energy of the key active components of Saposhnikovia divaricata docked with toll-like receptor proteins well. This bibliometric study aimed to help better comprehend the present state and make decisions from a macro viewpoint.

Keywords: Allergic diseases; Bibliometric analysis; Dendritic cells; Molecular docking; Visualization.

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Conflict of interest statement

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Ji Wang reports financial support was provided by 10.13039/501100001809National Natural Science Foundation of China. Ji Wang reports financial support was provided by National Administration of Traditional Chinese Medicine. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Flowchart of screening process and article framework.
Fig. 2
Fig. 2
The total annual trend of publications by year from 2004 to 2023. The solid line in orange represents the number of publications (Np) of all research types; the solid line in blue represents the Np of basic research; the solid line in green represents the Np of clinical research; the dashed line in yellow represents the Np of other research types. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3
Fig. 3
Countries/regions in research of the links between dentritic cells (DCs) and allergic diseases. (A) Geographical distribution of global output; (B) Annual output trend of the top ten productive countries. The pillar's height shows the number of papers. The higher the pillar, the more the number of papers issued in that country. The pillar's colours and forms represent diffierent countries/regions; (C) Visual cluster analysis of cooperation among countries/regions. The different colours of the nodes represent different countries/regions, with larger nodes meaning more frequently countries/regions; (D) Timeline visualization of cooperation among countries. Countries/regions in yellow carried out research in this field later than in blue. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4
Fig. 4
The yearly number of global citations for papers having a high global citation score (GCS). The pillar's height shows the number of citations. The higher the pillar, the more the number of citations of the paper. The pillar's colours and forms represent diffierent papers. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5
Fig. 5
Visualization of the co-cited reference analysis. (A) Co-occurrence network of co-cited reference. The different colours of the nodes represent different references, with larger nodes meaning more frequent co-cited references; (B) Top twenty representative burst co-cited references. The different colours of line segments represent different states. Red represents burst years of co-cited references; cyan represents the co-cited references are not in a burst state; light cyan represents the co-cited references hasn't been occurrent; (C) Timeline distribution of cluster analysis of the co-cited references. The different colours of lines represent different references and keywords clusters. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6
Fig. 6
Visualization of the keywords analysis. (A) Network of keywords. The different colours of the nodes represent different keywords, with larger nodes meaning more frequent keywords; (B) Timeline visualization of keywords. Keywords in yellow appeared later than that in blue; (C) Top twenty representative burst keywords. The different colours of line segments represent different states. Red represents burst years of keywords; cyan represents the keywords are not in a burst state; light cyan represents the keywords hasn't been occurrent; (D) Timeline distribution of cluster analysis of the keywords. The different colours of lines represent different keywords clusters. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 7
Fig. 7
Visualization of the peak years. (A) Network on keywords of the papers published in 2011; (B) Network on keywords of the papers published in 2017; (C) The top ten productive categories in 2011; (D) The top ten productive categories in 2017.
Fig. 8
Fig. 8
Molecular docking. Binding mode of (A) Toll-like receptor 1 (TLR1) with Cleomiscosin A; (B) TLR1 with Vanillic acid; (C) TLR1 with Prim-O-glucosylcimifugin; (D) TLR1 with 5-O-Methylvisammioside; (E) TLR3 with Cleomiscosin A; (F) TLR3 with Vanillic acid; (G) TLR3 with Prim-O-glucosylcimifugin; (H) TLR3 with 5-O-Methylvisammioside.
Fig. 9
Fig. 9
Bibliographic coupling analysis of the links between dentritic cells (DCs) and allergic diseases. The different colours of the nodes represent different categories (countries/regions; affiliations; authors; journals; documents), with larger nodes meaning more frequent occurrence. (A) Network of bibliographic coupling countries/regions; (B) Network of bibliographic coupling affiliations; (C) Network of bibliographic coupling authors; (D) Network of bibliographic coupling journals; (E) Network of bibliographic coupling documents. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 10
Fig. 10
Mechanisms of thymic stromal lymphopoietin (TSLP) and Toll-like receptors (TLRs) pathways interacting with DCs in allergic diseases (created with BioRender.com).

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