Nathan Standifer

2.6k total citations
37 papers, 678 citations indexed

About

Nathan Standifer is a scholar working on Immunology, Oncology and Molecular Biology. According to data from OpenAlex, Nathan Standifer has authored 37 papers receiving a total of 678 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Immunology, 15 papers in Oncology and 8 papers in Molecular Biology. Recurrent topics in Nathan Standifer's work include Immune Cell Function and Interaction (10 papers), Cancer Immunotherapy and Biomarkers (9 papers) and T-cell and B-cell Immunology (9 papers). Nathan Standifer is often cited by papers focused on Immune Cell Function and Interaction (10 papers), Cancer Immunotherapy and Biomarkers (9 papers) and T-cell and B-cell Immunology (9 papers). Nathan Standifer collaborates with scholars based in United States, United Kingdom and France. Nathan Standifer's co-authors include Gerald T. Nepom, Paul L. Bollyky, C. Bruce Verchere, Rusung Tan, Constadina Panagiotopoulos, Qin Ouyang, Carla J. Greenbaum, William W. Kwok, Xinhui Ge and Eddie A. James and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Oncology and The Journal of Immunology.

In The Last Decade

Nathan Standifer

37 papers receiving 656 citations

Peers

Nathan Standifer
Libuse Jerabek United States
Olivia Lou United States
Christina Fleischmann United States
Susan Masewicz United States
Yaohong Tan United States
Nathan Standifer
Citations per year, relative to Nathan Standifer Nathan Standifer (= 1×) peers Audrey Baeyens

Countries citing papers authored by Nathan Standifer

Since Specialization
Citations

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

Fields of papers citing papers by Nathan Standifer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nathan Standifer

This figure shows the co-authorship network connecting the top 25 collaborators of Nathan Standifer. A scholar is included among the top collaborators of Nathan Standifer 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 Nathan Standifer. Nathan Standifer 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.
Tran, Ben, Mark Voskoboynik, Johanna C. Bendell, et al.. (2024). A phase 1 study of the CD40 agonist MEDI5083 in combination with durvalumab in patients with advanced solid tumors. Immunotherapy. 16(11). 759–774. 5 indexed citations
2.
Yarchoan, Mark, John D. Powderly, Bruno R. Bastos, et al.. (2024). First-in-human Phase I Trial of TPST-1120, an Inhibitor of PPARα, as Monotherapy or in Combination with Nivolumab, in Patients with Advanced Solid Tumors. Cancer Research Communications. 4(4). 1100–1110. 11 indexed citations
3.
Patel, Sandip Pravin, Teresa Alonso‐Gordoa, Susana Banerjee, et al.. (2024). Phase 1/2 study of monalizumab plus durvalumab in patients with advanced solid tumors. Journal for ImmunoTherapy of Cancer. 12(2). e007340–e007340. 25 indexed citations
4.
Song, Xuyang, Robin Kate Kelley, Michelle Green, et al.. (2023). Modeling of Proliferating CD4 and CD8 T‐Cell Changes to Tremelimumab Exposure in Patients with Unresectable Hepatocellular Carcinoma. Clinical Pharmacology & Therapeutics. 114(4). 874–882. 2 indexed citations
5.
Song, Xuyang, Robin Kate Kelley, Anis A. Khan, et al.. (2022). Exposure-Response Analyses of Tremelimumab Monotherapy or in Combination with Durvalumab in Patients with Unresectable Hepatocellular Carcinoma. Clinical Cancer Research. 29(4). 754–763. 21 indexed citations
6.
Bouquet, Jérôme, David Y. Oh, Timothy J. Looney, et al.. (2021). Early changes in the circulating T cells are associated with clinical outcomes after PD-L1 blockade by durvalumab in advanced NSCLC patients. Cancer Immunology Immunotherapy. 70(7). 2095–2102. 17 indexed citations
7.
Balmanoukian, Ani Sarkis, Jeffrey R. Infante, Raid Aljumaily, et al.. (2020). Safety and Clinical Activity of MEDI1873, a Novel GITR Agonist, in Advanced Solid Tumors. Clinical Cancer Research. 26(23). 6196–6203. 40 indexed citations
9.
Sun, Yongliang, Thomas W. McCloskey, Thomas McIntosh, et al.. (2020). Best practices for optimization and validation of flow cytometry‐based receptor occupancy assays. Cytometry Part B Clinical Cytometry. 100(1). 63–71. 5 indexed citations
10.
Vicini, Paolo, Nathan Standifer, & Timothy P. Hickling. (2019). Recruiting the Immune System Against Disease: Lessons for Clinical and Systems Pharmacology. CPT Pharmacometrics & Systems Pharmacology. 8(7). 436–439. 2 indexed citations
11.
Kreitman, Robert J., Claire Dearden, Pier Luigi Zinzani, et al.. (2018). Moxetumomab pasudotox in heavily pretreated patients with relapsed/refractory hairy cell leukemia: Results of a pivotal international study.. Journal of Clinical Oncology. 36(15_suppl). 7004–7004. 2 indexed citations
12.
Standifer, Nathan, et al.. (2010). Maintenance of Immune Tolerance to a Neo-Self Acetylcholine Receptor Antigen with Aging: Implications for Late-Onset Autoimmunity. The Journal of Immunology. 184(11). 6067–6075. 8 indexed citations
13.
Sanda, Srinath, Jenna Bollyky, Nathan Standifer, et al.. (2010). Short-term IL-1β blockade reduces monocyte CD11b integrin expression in an IL-8 dependent fashion in patients with type 1 diabetes. Clinical Immunology. 136(2). 170–173. 17 indexed citations
14.
Standifer, Nathan, et al.. (2009). Changes in autoreactive T cell avidity during type 1 diabetes development. Clinical Immunology. 132(3). 312–320. 27 indexed citations
15.
Standifer, Nathan, et al.. (2007). Discrete T Cell Populations with Specificity for a Neo-Self-Antigen Bear Distinct Imprints of Tolerance. The Journal of Immunology. 178(6). 3544–3550. 1 indexed citations
16.
Ouyang, Qin, Nathan Standifer, Huilian Qin, et al.. (2006). Recognition of HLA Class I–Restricted β-Cell Epitopes in Type 1 Diabetes. Diabetes. 55(11). 3068–3074. 84 indexed citations
17.
Standifer, Nathan, et al.. (2005). Cytotoxic herpes simplex type 2‐specific, DQ0602‐restricted CD4+ T‐cell clones show alloreactivity to DQ0601. Immunology. 117(3). 350–357. 3 indexed citations
18.
Standifer, Nathan, Ellen Kraig, & Anthony J. Infante. (2003). A hierarchy of T cell receptor motifs determines responsiveness to the immunodominant epitope in experimental autoimmune myasthenia gravis. Journal of Neuroimmunology. 145(1-2). 68–76. 4 indexed citations
19.
Standifer, Nathan & John D. Madsen. (1997). The effect of drying period on the germination of Eurasian watermilfoil seeds.. Journal of Aquatic Plant Management. 35. 35–36. 5 indexed citations
20.
Kraig, Ellen, et al.. (1996). Restricted T cell receptor repertoire for acetylcholine receptor in murine myasthenia gravis. Journal of Neuroimmunology. 71(1-2). 87–95. 12 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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