Jared Kushner

1.9k total citations · 1 hit paper
17 papers, 1.4k citations indexed

About

Jared Kushner is a scholar working on Molecular Biology, Cardiology and Cardiovascular Medicine and Cell Biology. According to data from OpenAlex, Jared Kushner has authored 17 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 10 papers in Cardiology and Cardiovascular Medicine and 4 papers in Cell Biology. Recurrent topics in Jared Kushner's work include Ion channel regulation and function (8 papers), Cardiac electrophysiology and arrhythmias (8 papers) and Receptor Mechanisms and Signaling (5 papers). Jared Kushner is often cited by papers focused on Ion channel regulation and function (8 papers), Cardiac electrophysiology and arrhythmias (8 papers) and Receptor Mechanisms and Signaling (5 papers). Jared Kushner collaborates with scholars based in United States, United Kingdom and Portugal. Jared Kushner's co-authors include Shahin Rafii, David M. Valenzuela, Andrew Murphy, George D. Yancopoulos, Muhamed Baljević, Nicholas W. Gale, Sergey V. Shmelkov, Till Milde, Adı́lia Hormigo and Steven O. Marx and has published in prestigious journals such as Nature, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Jared Kushner

16 papers receiving 1.4k citations

Hit Papers

CD133 expression is not restricted to stem cells, and bot... 2008 2026 2014 2020 2008 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jared Kushner United States 11 707 641 235 223 196 17 1.4k
Eleanor Y. M. Sum Australia 14 849 1.2× 268 0.4× 73 0.3× 66 0.3× 133 0.7× 18 1.3k
Jessica N. Cleck United States 7 490 0.7× 161 0.3× 249 1.1× 28 0.1× 90 0.5× 8 970
Anna Robeva United States 18 870 1.2× 206 0.3× 659 2.8× 45 0.2× 71 0.4× 40 1.9k
Si Ho Choi United States 20 1.0k 1.5× 119 0.2× 81 0.3× 55 0.2× 108 0.6× 45 1.3k
Georg von Jonquières Australia 21 775 1.1× 155 0.2× 395 1.7× 17 0.1× 134 0.7× 35 1.3k
Miguel García‐Guzmán United States 21 891 1.3× 158 0.2× 331 1.4× 84 0.4× 60 0.3× 32 1.9k
Mei Ding China 16 535 0.8× 85 0.1× 105 0.4× 66 0.3× 62 0.3× 87 967
Yiai Tong Canada 24 1.0k 1.4× 422 0.7× 388 1.7× 31 0.1× 198 1.0× 41 1.7k
Kevin C. Ess United States 27 1.2k 1.6× 322 0.5× 325 1.4× 22 0.1× 84 0.4× 59 2.2k
María Dolores Gutiérrez‐López Spain 22 416 0.6× 116 0.2× 179 0.8× 20 0.1× 94 0.5× 34 1.3k

Countries citing papers authored by Jared Kushner

Since Specialization
Citations

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

Fields of papers citing papers by Jared Kushner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jared Kushner

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

All Works

17 of 17 papers shown
1.
Tzimas, Christos, Ilaria Russo, Wen Dun, et al.. (2025). Cardiomyocyte GC1 Mediates Estrogenic Angiogenesis in Right Heart Remodeling. Circulation Research. 137(12). 1407–1421.
2.
Hegyi, Bence, Lin Yang, Bi-Xing Chen, et al.. (2025). De novo design of a peptide modulator to reverse sodium channel dysfunction linked to cardiac arrhythmias and epilepsy. Cell. 188(22). 6170–6185.e19. 1 indexed citations
3.
Wu, Wei, Qi Jin, Cecilia Östlund, et al.. (2024). mTOR Inhibition Prolongs Survival and Has Beneficial Effects on Heart Function After Onset of Lamin A/C Gene Mutation Cardiomyopathy in Mice. Circulation Heart Failure. 17(4). 5 indexed citations
4.
Kushner, Jared, Guoxia Liu, Gary A. Bradshaw, et al.. (2022). Detecting Cardiovascular Protein-Protein Interactions by Proximity Proteomics. Circulation Research. 130(2). 273–287. 11 indexed citations
5.
Liu, Guoxia, et al.. (2021). Probing ion channel neighborhoods using proximity proteomics. Methods in enzymology on CD-ROM/Methods in enzymology. 654. 115–136. 5 indexed citations
6.
Avula, Uma Mahesh R., et al.. (2021). Theoretical Models and Computational Analysis of Action Potential Dispersion for Cardiac Arrhythmia Risk Stratification. Frontiers in Cardiovascular Medicine. 8. 649489–649489. 3 indexed citations
7.
Kushner, Jared, Arianne Papa, & Steven O. Marx. (2021). Use of Proximity Labeling in Cardiovascular Research. JACC Basic to Translational Science. 6(7). 598–609. 4 indexed citations
8.
Papa, Arianne, Jared Kushner, & Steven O. Marx. (2021). Adrenergic Regulation of Calcium Channels in the Heart. Annual Review of Physiology. 84(1). 285–306. 43 indexed citations
9.
Liu, Guoxia, Arianne Papa, Alexander N. Katchman, et al.. (2020). Mechanism of adrenergic CaV1.2 stimulation revealed by proximity proteomics. Nature. 577(7792). 695–700. 158 indexed citations
10.
Dridi, Haikel, Wei Wu, Steven Reiken, et al.. (2020). Ryanodine receptor remodeling in cardiomyopathy and muscular dystrophy caused by lamin A/C gene mutation. Human Molecular Genetics. 29(24). 3919–3934. 20 indexed citations
11.
Papa, Arianne, Jared Kushner, Jessica A. Hennessey, et al.. (2020). Adrenergic Ca V 1.2 Activation via Rad Phosphorylation Converges at α 1C I-II Loop. Circulation Research. 128(1). 76–88. 37 indexed citations
12.
Kushner, Jared, Xavier Ferrer, & Steven O. Marx. (2019). Roles and Regulation of Voltage-gated Calcium Channels in Arrhythmias. Journal of Innovations in Cardiac Rhythm Management. 10(10). 3874–3880. 11 indexed citations
13.
Yang, Lin, Alexander N. Katchman, Jared Kushner, et al.. (2018). Cardiac CaV1.2 channels require β subunits for β-adrenergic–mediated modulation but not trafficking. Journal of Clinical Investigation. 129(2). 647–658. 44 indexed citations
14.
Katchman, Alexander N., Lin Yang, Sergey I. Zakharov, et al.. (2017). Proteolytic cleavage and PKA phosphorylation of α 1C subunit are not required for adrenergic regulation of Ca V 1.2 in the heart. Proceedings of the National Academy of Sciences. 114(34). 9194–9199. 31 indexed citations
15.
Wan, Elaine Y., Jared Kushner, Sergey Zakharov, et al.. (2013). Reduced vascular smooth muscle BK channel current underlies heart failure‐induced vasoconstriction in mice. The FASEB Journal. 27(5). 1859–1867. 20 indexed citations
16.
Shmelkov, Sergey V., Adı́lia Hormigo, Deqiang Jing, et al.. (2010). Slitrk5 deficiency impairs corticostriatal circuitry and leads to obsessive-compulsive–like behaviors in mice. Nature Medicine. 16(5). 598–602. 258 indexed citations
17.
Shmelkov, Sergey V., Jason M. Butler, Andrea T. Hooper, et al.. (2008). CD133 expression is not restricted to stem cells, and both CD133+ and CD133– metastatic colon cancer cells initiate tumors. Journal of Clinical Investigation. 118(6). 2111–20. 731 indexed citations breakdown →

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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