David H. Gardner

1.5k total citations
18 papers, 1.1k citations indexed

About

David H. Gardner is a scholar working on Immunology, Oncology and Physiology. According to data from OpenAlex, David H. Gardner has authored 18 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Immunology, 5 papers in Oncology and 4 papers in Physiology. Recurrent topics in David H. Gardner's work include T-cell and B-cell Immunology (9 papers), Immune Cell Function and Interaction (8 papers) and Immunotherapy and Immune Responses (4 papers). David H. Gardner is often cited by papers focused on T-cell and B-cell Immunology (9 papers), Immune Cell Function and Interaction (8 papers) and Immunotherapy and Immune Responses (4 papers). David H. Gardner collaborates with scholars based in United Kingdom, Switzerland and Italy. David H. Gardner's co-authors include Francesca Barone, Saba Nayar, David M. Sansom, Louisa Jeffery, Christopher D. Buckley, Sanjiv A. Luther, Charlotte G. Smith, Tie Zheng Hou, Zoe Briggs and Karim Raza and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Immunology and PLoS ONE.

In The Last Decade

David H. Gardner

17 papers receiving 1.1k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David H. Gardner United Kingdom 15 620 333 223 175 154 18 1.1k
Jennifer Y. Wang United States 8 644 1.0× 102 0.3× 154 0.7× 174 1.0× 154 1.0× 27 993
Jason R. Lees United States 15 673 1.1× 254 0.8× 132 0.6× 46 0.3× 172 1.1× 27 1.2k
Patrick S. Asmawidjaja Netherlands 16 793 1.3× 189 0.6× 280 1.3× 82 0.5× 159 1.0× 36 1.3k
Kristīne Oļeiņika United Kingdom 8 688 1.1× 239 0.7× 83 0.4× 93 0.5× 292 1.9× 12 1.1k
Amerigo Santoro Italy 12 463 0.7× 161 0.5× 120 0.5× 178 1.0× 153 1.0× 15 1.0k
Michele Czajkowski France 5 556 0.9× 160 0.5× 99 0.4× 109 0.6× 236 1.5× 7 1.3k
Kunihiko Maeda Japan 14 286 0.5× 162 0.5× 182 0.8× 69 0.4× 141 0.9× 38 732
Antigoni Triantafyllopoulou United States 11 443 0.7× 188 0.6× 56 0.3× 156 0.9× 254 1.6× 19 911
Adriana M. C. Mus Netherlands 13 584 0.9× 126 0.4× 244 1.1× 56 0.3× 105 0.7× 25 927
Guadalupe Lima Mexico 19 429 0.7× 171 0.5× 60 0.3× 118 0.7× 140 0.9× 54 821

Countries citing papers authored by David H. Gardner

Since Specialization
Citations

This map shows the geographic impact of David H. Gardner's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David H. Gardner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David H. Gardner more than expected).

Fields of papers citing papers by David H. Gardner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David H. Gardner. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David H. Gardner. The network helps show where David H. Gardner may publish in the future.

Co-authorship network of co-authors of David H. Gardner

This figure shows the co-authorship network connecting the top 25 collaborators of David H. Gardner. A scholar is included among the top collaborators of David H. Gardner based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David H. Gardner. David H. Gardner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Mariette, Xavier, Francesca Barone, Chiara Baldini, et al.. (2022). A randomized, phase II study of sequential belimumab and rituximab in primary Sjögren’s syndrome. JCI Insight. 7(23). 70 indexed citations
2.
3.
Soskic, Blagoje, Louisa Jeffery, Alan Kennedy, et al.. (2021). CD80 on Human T Cells Is Associated With FoxP3 Expression and Supports Treg Homeostasis. Frontiers in Immunology. 11. 577655–577655. 27 indexed citations
4.
Nayar, Saba, et al.. (2021). Stromal cells in tertiary lymphoid structures: Architects of autoimmunity. Immunological Reviews. 302(1). 184–195. 33 indexed citations
5.
Pucino, Valentina, David H. Gardner, & Benjamin A. Fisher. (2020). Rationale for CD40 pathway blockade in autoimmune rheumatic disorders. The Lancet Rheumatology. 2(5). e292–e301. 9 indexed citations
6.
Nayar, Saba, Joana Campos, Charlotte G. Smith, et al.. (2019). Immunofibroblasts are pivotal drivers of tertiary lymphoid structure formation and local pathology. Proceedings of the National Academy of Sciences. 116(27). 13490–13497. 138 indexed citations
7.
Berardicurti, Onorina, et al.. (2019). The role of stroma and epithelial cells in primary Sjögren’s syndrome. Lara D. Veeken. 60(8). 3503–3512. 16 indexed citations
8.
Nayar, Saba, Joana Campos, Charlotte G. Smith, et al.. (2018). Phosphatidylinositol 3-kinase delta pathway: a novel therapeutic target for Sjögren's syndrome. Annals of the Rheumatic Diseases. 78(2). 249–260. 37 indexed citations
9.
Mueller, Christopher G., Saba Nayar, David H. Gardner, & Francesca Barone. (2018). Cellular and Vascular Components of Tertiary Lymphoid Structures. Methods in molecular biology. 1845. 17–30. 14 indexed citations
10.
Pipi, Elena, Saba Nayar, David H. Gardner, et al.. (2018). Tertiary Lymphoid Structures: Autoimmunity Goes Local. Frontiers in Immunology. 9. 1952–1952. 133 indexed citations
11.
Nayar, Saba, Joana Campos, Leyre Navarro‐Núñez, et al.. (2016). Bimodal Expansion of the Lymphatic Vessels Is Regulated by the Sequential Expression of IL-7 and Lymphotoxin α1β2 in Newly Formed Tertiary Lymphoid Structures. The Journal of Immunology. 197(5). 1957–1967. 38 indexed citations
12.
Barone, Francesca, et al.. (2016). Stromal Fibroblasts in Tertiary Lymphoid Structures: A Novel Target in Chronic Inflammation. Frontiers in Immunology. 7. 477–477. 136 indexed citations
13.
Jeffery, Louisa, Omar Qureshi, David H. Gardner, et al.. (2015). Vitamin D Antagonises the Suppressive Effect of Inflammatory Cytokines on CTLA-4 Expression and Regulatory Function. PLoS ONE. 10(7). e0131539–e0131539. 44 indexed citations
14.
Gardner, David H., Louisa Jeffery, Blagoje Soskic, et al.. (2015). 1,25(OH)2D3 Promotes the Efficacy of CD28 Costimulation Blockade by Abatacept. The Journal of Immunology. 195(6). 2657–2665. 17 indexed citations
15.
Gardner, David H., Louisa Jeffery, & David M. Sansom. (2014). Understanding the CD28/CTLA-4 (CD152) Pathway and Its Implications for Costimulatory Blockade. American Journal of Transplantation. 14(9). 1985–1991. 88 indexed citations
16.
Jeffery, Louisa, Alice M. Wood, Omar Qureshi, et al.. (2012). Availability of 25-Hydroxyvitamin D3 to APCs Controls the Balance between Regulatory and Inflammatory T Cell Responses. The Journal of Immunology. 189(11). 5155–5164. 166 indexed citations
17.
Torr, Elizabeth, David H. Gardner, D M Goodall, et al.. (2011). Apoptotic cell-derived ICAM-3 promotes both macrophage chemoattraction to and tethering of apoptotic cells. Cell Death and Differentiation. 19(4). 671–679. 84 indexed citations
18.
Garcia, Christine, David H. Gardner, & Kaaren K. Reichard. (2008). CD163: A Specific Immunohistochemical Marker for Acute Myeloid Leukemia With Monocytic Differentiation. Applied immunohistochemistry & molecular morphology. 16(5). 417–421. 14 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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