Thomas Delong

2.2k total citations · 2 hit papers
30 papers, 1.5k citations indexed

About

Thomas Delong is a scholar working on Genetics, Surgery and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Thomas Delong has authored 30 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Genetics, 19 papers in Surgery and 14 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Thomas Delong's work include Diabetes and associated disorders (24 papers), Pancreatic function and diabetes (18 papers) and Diabetes Management and Research (13 papers). Thomas Delong is often cited by papers focused on Diabetes and associated disorders (24 papers), Pancreatic function and diabetes (18 papers) and Diabetes Management and Research (13 papers). Thomas Delong collaborates with scholars based in United States, Canada and United Kingdom. Thomas Delong's co-authors include Kathryn Haskins, Rocky L. Baker, Brenda Bradley, Gene Barbour, Roger Powell, Timothy A. Wiles, Nichole Reisdorph, Richard Reisdorph, Michael Armstrong and Maki Nakayama and has published in prestigious journals such as Science, Journal of Biological Chemistry and Nature reviews. Immunology.

In The Last Decade

Thomas Delong

26 papers receiving 1.5k citations

Hit Papers

Pathogenic CD4 T cells in type 1 diabetes recognize epito... 2016 2026 2019 2022 2016 2024 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas Delong United States 17 1.1k 783 624 606 229 30 1.5k
Rocky L. Baker United States 16 900 0.8× 687 0.9× 499 0.8× 529 0.9× 196 0.9× 26 1.3k
Helle Markholst Denmark 18 493 0.4× 305 0.4× 179 0.3× 459 0.8× 201 0.9× 51 933
P. Lehmann United States 10 358 0.3× 219 0.3× 171 0.3× 426 0.7× 163 0.7× 13 873
L. Peterson United States 8 392 0.4× 213 0.3× 116 0.2× 282 0.5× 124 0.5× 14 589
Evie Melanitou France 13 297 0.3× 153 0.2× 176 0.3× 212 0.3× 237 1.0× 30 674
Alexis Styche United States 13 322 0.3× 212 0.3× 148 0.2× 284 0.5× 183 0.8× 19 693
K Terazono Japan 7 250 0.2× 561 0.7× 157 0.3× 56 0.1× 282 1.2× 7 827
Robert Lakomy United States 13 189 0.2× 122 0.2× 74 0.1× 371 0.6× 173 0.8× 18 673
Jee Yun Han South Korea 10 237 0.2× 131 0.2× 88 0.1× 71 0.1× 249 1.1× 12 558
Jonathan Rud United States 5 160 0.1× 230 0.3× 73 0.1× 172 0.3× 262 1.1× 8 618

Countries citing papers authored by Thomas Delong

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Delong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas Delong

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Delong. A scholar is included among the top collaborators of Thomas Delong 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 Thomas Delong. Thomas Delong 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.
Hohenstein, Anita C., Mylinh Dang, Kathleen Waugh, et al.. (2025). Novel T-Cell Reactivities to Hybrid Insulin Peptides in Islet Autoantibody–Positive At-Risk Individuals. Diabetes. 74(6). 933–942.
2.
Nicholson, K. M., Mylinh Dang, Janet M. Wenzlau, et al.. (2025). Strategic Reduction of Hybrid Insulin Peptide Formation Significantly Delays Diabetes Onset in NOD Mice. Diabetes. 75(1). 115–123.
3.
Matan-Lithwick, Stuart, Thomas Delong, Shreejoy J. Tripathy, et al.. (2025). A Transcriptomic Signature of Depressive Symptoms in Late Life. Biological Psychiatry Global Open Science. 5(3). 100448–100448.
4.
James, Eddie A., et al.. (2024). The insulin secretory granule is a hotspot for autoantigen formation in type 1 diabetes. Diabetologia. 67(8). 1507–1516. 6 indexed citations
5.
Herold, Kevan C., et al.. (2024). The immunology of type 1 diabetes. Nature reviews. Immunology. 24(6). 435–451. 81 indexed citations breakdown →
6.
Delong, Thomas & Maki Nakayama. (2024). Epitope Hierarchy in Type 1 Diabetes Pathogenesis. Cold Spring Harbor Perspectives in Medicine. 15(11). a041594–a041594.
7.
Dang, Mylinh, Cole R. Michel, Roger Powell, et al.. (2023). Hybrid insulin peptide isomers spontaneously form in pancreatic beta-cells from an aspartic anhydride intermediate. Journal of Biological Chemistry. 299(11). 105264–105264. 5 indexed citations
8.
Wenzlau, Janet M., Gene Barbour, Mylinh Dang, et al.. (2022). Insulin B-chain hybrid peptides are agonists for T cells reactive to insulin B:9-23 in autoimmune diabetes. Frontiers in Immunology. 13. 926650–926650. 11 indexed citations
9.
Parras, Daniel, et al.. (2021). Recognition of Multiple Hybrid Insulin Peptides by a Single Highly Diabetogenic T-Cell Receptor. Frontiers in Immunology. 12. 737428–737428. 12 indexed citations
10.
Derua, Rita, et al.. (2020). Identification of Deamidated Peptides in Cytokine-Exposed MIN6 Cells through LC−MS/MS Using a Shortened Digestion Time and Inspection of MS2 Spectra. Journal of Proteome Research. 20(2). 1405–1414. 7 indexed citations
11.
Baker, Rocky L., Marynette Rihanek, Anita C. Hohenstein, et al.. (2019). Hybrid Insulin Peptides Are Autoantigens in Type 1 Diabetes. Diabetes. 68(9). 1830–1840. 68 indexed citations
12.
Wiles, Timothy A. & Thomas Delong. (2019). HIPs and HIP-reactive T cells. Clinical & Experimental Immunology. 198(3). 306–313. 16 indexed citations
13.
Wiles, Timothy A., Roger Powell, Cole R. Michel, et al.. (2018). Identification of Hybrid Insulin Peptides (HIPs) in Mouse and Human Islets by Mass Spectrometry. Journal of Proteome Research. 18(3). 814–825. 63 indexed citations
14.
Delong, Thomas, Timothy A. Wiles, Rocky L. Baker, et al.. (2016). Pathogenic CD4 T cells in type 1 diabetes recognize epitopes formed by peptide fusion. Science. 351(6274). 711–714. 379 indexed citations breakdown →
15.
Wiles, Timothy A., Thomas Delong, Rocky L. Baker, et al.. (2016). An insulin-IAPP hybrid peptide is an endogenous antigen for CD4 T cells in the non-obese diabetic mouse. Journal of Autoimmunity. 78. 11–18. 75 indexed citations
16.
Gottlieb, Peter A., Thomas Delong, Rocky L. Baker, et al.. (2013). Chromogranin A is a T cell antigen in human type 1 diabetes. Journal of Autoimmunity. 50. 38–41. 67 indexed citations
17.
Delong, Thomas, Rocky L. Baker, Jing He, & Kathryn Haskins. (2012). Novel autoantigens for diabetogenic CD4 T cells in autoimmune diabetes. Immunologic Research. 55(1-3). 167–172. 15 indexed citations
18.
Delong, Thomas, Rocky L. Baker, Jing He, et al.. (2012). Diabetogenic T-Cell Clones Recognize an Altered Peptide of Chromogranin A. Diabetes. 61(12). 3239–3246. 90 indexed citations
19.
Stadinski, Brian D., Thomas Delong, Nichole Reisdorph, et al.. (2010). Chromogranin A is an autoantigen in type 1 diabetes. Nature Immunology. 11(3). 225–231. 286 indexed citations
20.
Delong, Thomas, et al.. (2003). Functional differentiation and selective inactivation of multiple Saccharomyces cerevisiae genes involved in very-long-chain fatty acid synthesis. Molecular Genetics and Genomics. 269(2). 290–298. 51 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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