John Blakemore

873 total citations
11 papers, 715 citations indexed

About

John Blakemore is a scholar working on Immunology, Surgery and Molecular Biology. According to data from OpenAlex, John Blakemore has authored 11 papers receiving a total of 715 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Immunology, 5 papers in Surgery and 2 papers in Molecular Biology. Recurrent topics in John Blakemore's work include Phagocytosis and Immune Regulation (4 papers), Atherosclerosis and Cardiovascular Diseases (3 papers) and Immune cells in cancer (2 papers). John Blakemore is often cited by papers focused on Phagocytosis and Immune Regulation (4 papers), Atherosclerosis and Cardiovascular Diseases (3 papers) and Immune cells in cancer (2 papers). John Blakemore collaborates with scholars based in United States, Canada and China. John Blakemore's co-authors include MacRae F. Linton, Patricia G. Yancey, Lei Ding, Daping Fan, Sergio Fazio, Sergio Fazio, Youmin Zhang, Hagai Tavori, Tao Huan and Youmin Zhang and has published in prestigious journals such as Circulation, Journal of Clinical Investigation and Nature Communications.

In The Last Decade

John Blakemore

11 papers receiving 707 citations

Peers

John Blakemore

IMMUN

SURGE

MB

EPIDE

CR

Jenny Freitag Germany

Michael Kapinsky Germany

Atsuyuki Nakata Japan

Barbara D. Summers United States

Yoshiaki Morita Japan

Geming Lu United States

Amrita Nandan India

Seishi Nagamori Japan

Chaitra Marathe United States

Gisela Vaitaitis United States

Jenny Freitag Germany

John Blakemore

647 ×2.0

IMMUN

108 ×0.4

SURGE

297 ×1.5

MB

96 ×0.8

EPIDE

146 ×1.6

CR

Citations per year, relative to John Blakemore John Blakemore (= 1×) peers Jenny Freitag

Countries citing papers authored by John Blakemore

Since Specialization

Citations

This map shows the geographic impact of John Blakemore'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 John Blakemore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Blakemore more than expected).

Fields of papers citing papers by John Blakemore

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John Blakemore. 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 John Blakemore. The network helps show where John Blakemore may publish in the future.

Co-authorship network of co-authors of John Blakemore

This figure shows the co-authorship network connecting the top 25 collaborators of John Blakemore. A scholar is included among the top collaborators of John Blakemore 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 John Blakemore. John Blakemore is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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11 of 11 papers shown

1.

Blakemore, John. (2023). Technology for Business. 1 indexed citations

2.

Yancey, Patricia G., John Blakemore, Youmin Zhang, et al.. (2021). Macrophage SR-BI modulates autophagy via VPS34 complex and PPARα transcription of Tfeb in atherosclerosis. Journal of Clinical Investigation. 131(7). 55 indexed citations

3.

Huan, Tao, Jiansheng Huang, Patricia G. Yancey, et al.. (2020). Scavenging of reactive dicarbonyls with 2-hydroxybenzylamine reduces atherosclerosis in hypercholesterolemic Ldlr−/− mice. Nature Communications. 11(1). 4084–4084. 50 indexed citations

4.

Tavori, Hagai, Yan Ru Su, Patricia G. Yancey, et al.. (2015). Macrophage apoAI protects against dyslipidemia-induced dermatitis and atherosclerosis without affecting HDL. Journal of Lipid Research. 56(3). 635–643. 25 indexed citations

5.

Huan, Tao, Patricia G. Yancey, Vladimir R. Babaev, et al.. (2015). Macrophage SR-BI mediates efferocytosis via Src/PI3K/Rac1 signaling and reduces atherosclerotic lesion necrosis. Journal of Lipid Research. 56(8). 1449–1460. 109 indexed citations

6.

Tavori, Hagai, Daping Fan, John Blakemore, et al.. (2013). Serum Proprotein Convertase Subtilisin/Kexin Type 9 and Cell Surface Low-Density Lipoprotein Receptor. Circulation. 127(24). 2403–2413. 158 indexed citations

7.

Yancey, Patricia G., Yu‐Qiang Ding, Daping Fan, et al.. (2011). Low-Density Lipoprotein Receptor–Related Protein 1 Prevents Early Atherosclerosis by Limiting Lesional Apoptosis and Inflammatory Ly-6C ^high Monocytosis. Circulation. 124(4). 454–464. 64 indexed citations

8.

Yancey, Patricia G., John Blakemore, Lei Ding, et al.. (2010). Macrophage LRP-1 Controls Plaque Cellularity by Regulating Efferocytosis and Akt Activation. Arteriosclerosis Thrombosis and Vascular Biology. 30(4). 787–795. 135 indexed citations

9.

Jayagopal, Ashwath, Yan Ru Su, John Blakemore, et al.. (2009). Quantum dot mediated imaging of atherosclerosis. Nanotechnology. 20(16). 165102–165102. 37 indexed citations

10.

Su, Yan, et al.. (2008). Lentiviral Transduction of ApoAI Into Hematopoietic Progenitor Cells and Macrophages. Arteriosclerosis Thrombosis and Vascular Biology. 28(8). 1439–1446. 17 indexed citations

11.

Arora, Vivek, et al.. (2000). Lentivirus Nef Specifically Activates Pak2. Journal of Virology. 74(23). 11081–11087. 64 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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