Shanmugam Nagarajan

3.0k total citations
62 papers, 2.3k citations indexed

About

Shanmugam Nagarajan is a scholar working on Molecular Biology, Immunology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Shanmugam Nagarajan has authored 62 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 27 papers in Immunology and 14 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Shanmugam Nagarajan's work include Monoclonal and Polyclonal Antibodies Research (14 papers), Atherosclerosis and Cardiovascular Diseases (9 papers) and Cell Adhesion Molecules Research (9 papers). Shanmugam Nagarajan is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (14 papers), Atherosclerosis and Cardiovascular Diseases (9 papers) and Cell Adhesion Molecules Research (9 papers). Shanmugam Nagarajan collaborates with scholars based in United States, China and Japan. Shanmugam Nagarajan's co-authors include Periasamy Selvaraj, Thomas M. Badger, Cheng‐Hui Xie, Xianli Wu, Cheng Zhu, Alexander G. Schauss, Jie Kang, Tong Wu, Jie Kang and Matthew Ferguson and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Journal of Clinical Investigation.

In The Last Decade

Shanmugam Nagarajan

61 papers receiving 2.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shanmugam Nagarajan United States 31 819 685 348 304 266 62 2.3k
M. Djavad Mossalayi France 27 524 0.6× 906 1.3× 407 1.2× 154 0.5× 144 0.5× 61 2.3k
Naoto Yamaguchi Japan 31 976 1.2× 604 0.9× 226 0.6× 71 0.2× 145 0.5× 105 2.5k
Charles Kunsch United States 24 2.2k 2.7× 1.2k 1.7× 391 1.1× 249 0.8× 78 0.3× 35 4.3k
Pedro A. Ruiz Switzerland 23 1.0k 1.3× 637 0.9× 454 1.3× 84 0.3× 68 0.3× 56 2.5k
Jun Guo China 31 1.1k 1.4× 1.2k 1.8× 338 1.0× 97 0.3× 70 0.3× 118 3.2k
Laura Gray United States 25 1.1k 1.3× 959 1.4× 194 0.6× 59 0.2× 450 1.7× 47 2.9k
Nicoletta Ferrari Italy 36 2.0k 2.4× 611 0.9× 127 0.4× 199 0.7× 89 0.3× 113 3.7k
Simon J.T. Mao Taiwan 27 903 1.1× 204 0.3× 215 0.6× 172 0.6× 146 0.5× 88 2.8k
Yuji Sato Japan 32 1.6k 2.0× 664 1.0× 863 2.5× 618 2.0× 554 2.1× 96 4.1k
Massimo Bortolotti Italy 26 902 1.1× 646 0.9× 182 0.5× 45 0.1× 191 0.7× 60 2.2k

Countries citing papers authored by Shanmugam Nagarajan

Since Specialization
Citations

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

Fields of papers citing papers by Shanmugam Nagarajan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shanmugam Nagarajan

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

All Works

20 of 20 papers shown
1.
Nagarajan, Uma M., James D. Sikes, Rajneesh Jha, et al.. (2019). Genital Chlamydia infection in hyperlipidemic mouse models exacerbates atherosclerosis. Atherosclerosis. 290. 103–110. 4 indexed citations
2.
Jiang, An, Hirohisa Okabe, Branimir Popovic, et al.. (2019). Loss of Wnt Secretion by Macrophages Promotes Hepatobiliary Injury after Administration of 3,5-Diethoxycarbonyl-1, 4-Dihydrocollidine Diet. American Journal Of Pathology. 189(3). 590–603. 24 indexed citations
3.
4.
Rahal, Omar, Heather L. Machado, Maria Theresa E. Montales, et al.. (2013). Dietary suppression of the mammary CD29hiCD24+ epithelial subpopulation and its cytokine/chemokine transcriptional signatures modifies mammary tumor risk in MMTV-Wnt1 transgenic mice. Stem Cell Research. 11(3). 1149–1162. 10 indexed citations
5.
Xie, Cheng‐Hui, Jie Kang, Zhimin Li, et al.. (2011). Velutin reduces lipopolysaccharide-induced proinflammatory cytokine TNF-{alpha} and IL-6 production by inhibiting NF-{kappa}B activation. The FASEB Journal. 25. 1 indexed citations
6.
Xie, Cheng‐Hui, Jie Kang, Jin‐Ran Chen, et al.. (2011). Lowbush blueberries inhibit scavenger receptors CD36 and SR-A expression and attenuate foam cell formation in ApoE-deficient mice. Food & Function. 2(10). 588–588. 14 indexed citations
7.
Xie, Cheng‐Hui, Jie Kang, Zhimin Li, et al.. (2011). The açaí flavonoid velutin is a potent anti-inflammatory agent: blockade of LPS-mediated TNF-α and IL-6 production through inhibiting NF-κB activation and MAPK pathway. The Journal of Nutritional Biochemistry. 23(9). 1184–1191. 162 indexed citations
8.
Xie, Cheng‐Hui, Jie Kang, Matthew Ferguson, et al.. (2011). Açaí juice attenuates atherosclerosis in ApoE deficient mice through antioxidant and anti-inflammatory activities. Atherosclerosis. 216(2). 327–333. 61 indexed citations
9.
Wu, Xianli, Jie Kang, Cheng‐Hui Xie, et al.. (2010). Dietary Blueberries Attenuate Atherosclerosis in Apolipoprotein E-Deficient Mice by Upregulating Antioxidant Enzyme Expression. Journal of Nutrition. 140(9). 1628–1632. 87 indexed citations
10.
Thampi, Prajitha, et al.. (2007). Dietary homocysteine promotes atherosclerosis in apoE-deficient mice by inducing scavenger receptors expression. Atherosclerosis. 197(2). 620–629. 38 indexed citations
11.
12.
Li, Ping, Ning Jiang, Shanmugam Nagarajan, et al.. (2007). Affinity and Kinetic Analysis of Fcγ Receptor IIIa (CD16a) Binding to IgG Ligands. Journal of Biological Chemistry. 282(9). 6210–6221. 45 indexed citations
13.
Nagarajan, Shanmugam, et al.. (2006). Soy Isoflavones Attenuate Human Monocyte Adhesion to Endothelial Cell–Specific CD54 by Inhibiting Monocyte CD11a. Journal of Nutrition. 136(9). 2384–2390. 14 indexed citations
14.
Wang, Yichong, et al.. (2006). B7-1-HSA (CD80-CD24), a recombinant hybrid costimulatory molecule retains ligand binding and costimulatory functions. Immunology Letters. 105(2). 185–192. 2 indexed citations
15.
Nagarajan, Shanmugam, Nimita Fifadara, & Periasamy Selvaraj. (2005). Signal-Specific Activation and Regulation of Human Neutrophil Fcγ Receptors. The Journal of Immunology. 174(9). 5423–5432. 16 indexed citations
16.
17.
Chen, Rui, Shanmugam Nagarajan, Uma Maheshwari, et al.. (2000). Impaired growth and elevated Fas receptor expression in PIGA+ stem cells in primary paroxysmal nocturnal hemoglobinuria. Journal of Clinical Investigation. 106(5). 689–696. 44 indexed citations
18.
Nagarajan, Shanmugam, et al.. (1995). Purification and optimization of functional reconstitution on the surface of leukemic cell lines of GPI-anchored Fcγ receptor III. Journal of Immunological Methods. 184(2). 241–251. 33 indexed citations
19.
Nagarajan, Shanmugam, et al.. (1995). Ligand Binding and Phagocytosis by CD16 (Fc γ Receptor III) Isoforms. Journal of Biological Chemistry. 270(43). 25762–25770. 77 indexed citations
20.

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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