Kenneth H. Dinnon

11.8k total citations · 1 hit paper
25 papers, 1.9k citations indexed

About

Kenneth H. Dinnon is a scholar working on Infectious Diseases, Animal Science and Zoology and Genetics. According to data from OpenAlex, Kenneth H. Dinnon has authored 25 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Infectious Diseases, 10 papers in Animal Science and Zoology and 5 papers in Genetics. Recurrent topics in Kenneth H. Dinnon's work include SARS-CoV-2 and COVID-19 Research (16 papers), Animal Virus Infections Studies (10 papers) and Viral gastroenteritis research and epidemiology (6 papers). Kenneth H. Dinnon is often cited by papers focused on SARS-CoV-2 and COVID-19 Research (16 papers), Animal Virus Infections Studies (10 papers) and Viral gastroenteritis research and epidemiology (6 papers). Kenneth H. Dinnon collaborates with scholars based in United States, India and Germany. Kenneth H. Dinnon's co-authors include Ralph S. Baric, Timothy P. Sheahan, Ariane J. Brown, Rachel L. Graham, Sarah R. Leist, Lisa E. Gralinski, Boyd L. Yount, Amy Sims, Alexandra Schäfer and Yixuan J. Hou and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Kenneth H. Dinnon

25 papers receiving 1.9k citations

Hit Papers

Broad spectrum antiviral remdesivir inhibits human endemi... 2019 2026 2021 2023 2019 100 200 300

Peers

Kenneth H. Dinnon
Trevor Scobey United States
Imke Steffen Germany
Paul W. Rothlauf United States
Huihui Mou United States
Javier A. Jaimes United States
Rita E. Chen United States
Kenneth H. Dinnon
Citations per year, relative to Kenneth H. Dinnon Kenneth H. Dinnon (= 1×) peers Adriaan H. de Wilde

Countries citing papers authored by Kenneth H. Dinnon

Since Specialization
Citations

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

Fields of papers citing papers by Kenneth H. Dinnon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kenneth H. Dinnon. 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 Kenneth H. Dinnon. The network helps show where Kenneth H. Dinnon may publish in the future.

Co-authorship network of co-authors of Kenneth H. Dinnon

This figure shows the co-authorship network connecting the top 25 collaborators of Kenneth H. Dinnon. A scholar is included among the top collaborators of Kenneth H. Dinnon 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 Kenneth H. Dinnon. Kenneth H. Dinnon 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.
Anderson, Elizabeth J., Sanjay Sarkar, Sharon Taft-Benz, et al.. (2024). Host Genetic Variation Impacts SARS-CoV-2 Vaccination Response in the Diversity Outbred Mouse Population. Vaccines. 12(1). 103–103. 2 indexed citations
2.
Fritch, Ethan J., Thomas S.K. Gilbert, Carrow I. Wells, et al.. (2023). Investigation of the Host Kinome Response to Coronavirus Infection Reveals PI3K/mTOR Inhibitors as Betacoronavirus Antivirals. Journal of Proteome Research. 22(10). 3159–3177. 9 indexed citations
3.
Amruta, Narayanappa, Saifudeen Ismael, Sarah R. Leist, et al.. (2022). Mouse Adapted SARS-CoV-2 (MA10) Viral Infection Induces Neuroinflammation in Standard Laboratory Mice. Viruses. 15(1). 114–114. 18 indexed citations
4.
Davis, Michael A., Julie Turnbull, Megan Knoll, et al.. (2022). A C57BL/6 Mouse Model of SARS-CoV-2 Infection Recapitulates Age- and Sex-Based Differences in Human COVID-19 Disease and Recovery. Vaccines. 11(1). 47–47. 16 indexed citations
5.
Hsieh, Ching‐Lin, Anne P. Werner, Sarah R. Leist, et al.. (2021). Stabilized coronavirus spike stem elicits a broadly protective antibody. Cell Reports. 37(5). 109929–109929. 55 indexed citations
6.
Dinnon, Kenneth H., Sarah R. Leist, Alexandra Schäfer, et al.. (2021). Publisher Correction: A mouse-adapted model of SARS-CoV-2 to test COVID-19 countermeasures. Nature. 590(7844). E22–E22. 7 indexed citations
7.
Adams, Lily E., Kenneth H. Dinnon, Yixuan J. Hou, et al.. (2021). Critical ACE2 Determinants of SARS-CoV-2 and Group 2B Coronavirus Infection and Replication. mBio. 12(2). 4 indexed citations
9.
Tostanoski, Lisa H., Lisa E. Gralinski, David R. Martinez, et al.. (2021). Protective Efficacy of Rhesus Adenovirus COVID-19 Vaccines against Mouse-Adapted SARS-CoV-2. Journal of Virology. 95(23). e0097421–e0097421. 13 indexed citations
10.
Sun, Weina, Stephen McCroskery, Wen‐Chun Liu, et al.. (2020). A Newcastle Disease Virus (NDV) Expressing a Membrane-Anchored Spike as a Cost-Effective Inactivated SARS-CoV-2 Vaccine. Vaccines. 8(4). 771–771. 59 indexed citations
11.
Leist, Sarah R., Kenneth H. Dinnon, Alexandra Schäfer, et al.. (2020). A Mouse-Adapted SARS-CoV-2 Induces Acute Lung Injury and Mortality in Standard Laboratory Mice. Cell. 183(4). 1070–1085.e12. 332 indexed citations
12.
Sun, Weina, Sarah R. Leist, Stephen McCroskery, et al.. (2020). Newcastle disease virus (NDV) expressing the spike protein of SARS-CoV-2 as a live virus vaccine candidate. EBioMedicine. 62. 103132–103132. 71 indexed citations
13.
Dinnon, Kenneth H., Sarah R. Leist, Alexandra Schäfer, et al.. (2020). A mouse-adapted model of SARS-CoV-2 to test COVID-19 countermeasures. Nature. 586(7830). 560–566. 381 indexed citations
14.
Hörner, Cindy, Christoph Schürmann, Aileen Ebenig, et al.. (2020). A highly immunogenic and effective measles virus-based Th1-biased COVID-19 vaccine. Proceedings of the National Academy of Sciences. 117(51). 32657–32666. 48 indexed citations
15.
Edwards, Caitlin E., Boyd L. Yount, Rachel L. Graham, et al.. (2020). Swine acute diarrhea syndrome coronavirus replication in primary human cells reveals potential susceptibility to infection. Proceedings of the National Academy of Sciences. 117(43). 26915–26925. 111 indexed citations
16.
Brown, Ariane J., John J. Won, Rachel L. Graham, et al.. (2019). Broad spectrum antiviral remdesivir inhibits human endemic and zoonotic deltacoronaviruses with a highly divergent RNA dependent RNA polymerase. Antiviral Research. 169. 104541–104541. 372 indexed citations breakdown →
17.
Menachery, Vineet D., Kenneth H. Dinnon, Boyd L. Yount, et al.. (2019). Trypsin Treatment Unlocks Barrier for Zoonotic Bat Coronavirus Infection. Journal of Virology. 94(5). 145 indexed citations
18.
Menachery, Vineet D., Lisa E. Gralinski, Hugh Mitchell, et al.. (2017). Middle East Respiratory Syndrome Coronavirus Nonstructural Protein 16 Is Necessary for Interferon Resistance and Viral Pathogenesis. mSphere. 2(6). 86 indexed citations
19.
Menachery, Vineet D., Hugh Mitchell, Adam S. Cockrell, et al.. (2017). MERS-CoV Accessory ORFs Play Key Role for Infection and Pathogenesis. mBio. 8(4). 111 indexed citations
20.
Gallichotte, Emily N., Kenneth H. Dinnon, Xin-Ni Lim, et al.. (2017). CD-loop Extension in Zika Virus Envelope Protein Key for Stability and Pathogenesis. The Journal of Infectious Diseases. 216(10). 1196–1204. 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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