Jonathan E. Zuckerman

7.3k total citations · 3 hit papers
68 papers, 5.2k citations indexed

About

Jonathan E. Zuckerman is a scholar working on Molecular Biology, Nephrology and Immunology. According to data from OpenAlex, Jonathan E. Zuckerman has authored 68 papers receiving a total of 5.2k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 20 papers in Nephrology and 10 papers in Immunology. Recurrent topics in Jonathan E. Zuckerman's work include Renal Diseases and Glomerulopathies (16 papers), RNA Interference and Gene Delivery (8 papers) and Advanced biosensing and bioanalysis techniques (7 papers). Jonathan E. Zuckerman is often cited by papers focused on Renal Diseases and Glomerulopathies (16 papers), RNA Interference and Gene Delivery (8 papers) and Advanced biosensing and bioanalysis techniques (7 papers). Jonathan E. Zuckerman collaborates with scholars based in United States, China and Italy. Jonathan E. Zuckerman's co-authors include Mark E. Davis, Chung Hang Jonathan Choi, Yun Yen, Antoni Ribas, Jeremy D. Heidel, Anthony W. Tolcher, David B. Seligson, Christopher A. Alabi, Mark E. Davis and Paul Webster and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Jonathan E. Zuckerman

54 papers receiving 5.2k citations

Hit Papers

Evidence of RNAi in human... 2010 2026 2015 2020 2010 2019 2021 500 1000 1.5k

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Jonathan E. Zuckerman 3.2k 1.3k 1.1k 624 394 68 5.2k
Zhiyong Wang 3.1k 1.0× 383 0.3× 443 0.4× 645 1.0× 1.1k 2.7× 145 5.2k
Marc A. M. J. van Zandvoort 1.4k 0.4× 348 0.3× 526 0.5× 373 0.6× 237 0.6× 85 4.4k
Abhijit De 3.0k 0.9× 678 0.5× 1.4k 1.3× 494 0.8× 910 2.3× 119 5.6k
Christoph Bremer 1.7k 0.5× 659 0.5× 2.3k 2.1× 722 1.2× 783 2.0× 115 6.0k
Gabriel Helmlinger 1.7k 0.5× 371 0.3× 896 0.8× 809 1.3× 743 1.9× 65 4.1k
Lawrence W. Dobrucki 1.5k 0.5× 876 0.7× 1.2k 1.1× 479 0.8× 400 1.0× 116 4.5k
Michael Donovan 3.0k 0.9× 216 0.2× 942 0.8× 945 1.5× 415 1.1× 137 4.9k
Giovanni Cuda 2.4k 0.7× 202 0.2× 1.2k 1.1× 478 0.8× 378 1.0× 176 5.1k
Anna Moore 2.5k 0.8× 1.4k 1.1× 1.7k 1.5× 515 0.8× 380 1.0× 123 6.9k
Tarik F. Massoud 2.4k 0.7× 771 0.6× 1.7k 1.5× 680 1.1× 312 0.8× 175 6.8k

Countries citing papers authored by Jonathan E. Zuckerman

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan E. Zuckerman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan E. Zuckerman. 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 Jonathan E. Zuckerman. The network helps show where Jonathan E. Zuckerman may publish in the future.

Co-authorship network of co-authors of Jonathan E. Zuckerman

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan E. Zuckerman. A scholar is included among the top collaborators of Jonathan E. Zuckerman 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 Jonathan E. Zuckerman. Jonathan E. Zuckerman 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.
Frazier, Eric, et al.. (2025). Dasatinib-induced renal (or chronic) thrombotic microangiopathy in a patient with chronic myeloid leukemia: A case report. SAGE Open Medical Case Reports. 13. 2050313X251322621–2050313X251322621.
2.
Armenian, Patil, et al.. (2025). Acute kidney injury and nephrotic syndrome caused by a “magic pill”. PubMed. 13(1). 59–65.
4.
Li, Yuzhu, Nir Pillar, Jingxi Li, et al.. (2024). Virtual histological staining of unlabeled autopsy tissue. Nature Communications. 15(1). 1684–1684. 18 indexed citations
6.
Zuckerman, Jonathan E., et al.. (2021). Vanquishing Vancomycin-Associated Acute Interstitial Nephritis. Journal of the American Society of Nephrology. 32(10S). 781–781.
7.
Haan, Kevin de, Yijie Zhang, Jonathan E. Zuckerman, et al.. (2021). Deep learning-based transformation of H&E stained tissues into special stains. Nature Communications. 12(1). 4884–4884. 185 indexed citations breakdown →
8.
Ettenger, Robert B., et al.. (2021). De novo lupus-like glomerulonephritis after pediatric non-kidney organ transplantation. Pediatric Nephrology. 37(1). 153–161.
9.
Chang, Yongen, Sheetal Desai, Jonathan E. Zuckerman, et al.. (2021). Complement-Mediated Thrombotic Microangiopathy Associated with Lupus Nephritis Treated with Eculizumab: A Case Report. Case Reports in Nephrology and Dialysis. 11(1). 95–102. 7 indexed citations
10.
Li, Ying, et al.. (2020). Evolution of altered tubular metabolism and mitochondrial function in sepsis-associated acute kidney injury. American Journal of Physiology-Renal Physiology. 319(2). F229–F244. 41 indexed citations
11.
Rivenson, Yair, Hongda Wang, Zhensong Wei, et al.. (2019). Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nature Biomedical Engineering. 3(6). 466–477. 406 indexed citations breakdown →
12.
Chong, Thomas, Dorina Gui, Nora Ostrzega, et al.. (2019). The California Telepathology Service: UCLA's Experience in Deploying a Regional Digital Pathology Subspecialty Consultation Network. Journal of Pathology Informatics. 10(1). 31–31. 24 indexed citations
13.
Zuckerman, Jonathan E., et al.. (2018). Acute Kidney Injury in a Patient Following Kidney Transplantation. American Journal of Kidney Diseases. 73(1). A15–A19. 2 indexed citations
14.
Duncan, Mark D., et al.. (2016). Small Cell Lung Cancer Presenting as a Cardiac Mass with Embolic Phenomena. The American Journal of Medicine. 130(2). e55–e57. 4 indexed citations
15.
Zuckerman, Jonathan E. & Mark E. Davis. (2013). Targeting Therapeutics to the Glomerulus With Nanoparticles. Advances in Chronic Kidney Disease. 20(6). 500–507. 60 indexed citations
16.
Zuckerman, Jonathan E., Chung Hang Jonathan Choi, Han Han, & Mark E. Davis. (2012). Polycation-siRNA nanoparticles can disassemble at the kidney glomerular basement membrane. Proceedings of the National Academy of Sciences. 109(8). 3137–3142. 307 indexed citations
17.
Rahman, Mohammad Aminur, A.R.M. Ruhul Amin, Xu Wang, et al.. (2012). Systemic delivery of siRNA nanoparticles targeting RRM2 suppresses head and neck tumor growth. Journal of Controlled Release. 159(3). 384–392. 72 indexed citations
18.
Tan, Frederick J., Jonathan E. Zuckerman, Robert C. Wells, & R. Blake Hill. (2010). The C. elegans B‐cell lymphoma 2 (Bcl‐2) homolog cell death abnormal 9 (CED‐9) associates with and remodels LIPID membranes. Protein Science. 20(1). 62–74. 5 indexed citations
19.
Davis, Mark E., Jonathan E. Zuckerman, Chung Hang Jonathan Choi, et al.. (2010). Evidence of RNAi in humans from systemically administered siRNA via targeted nanoparticles. Nature. 464(7291). 1067–1070. 1943 indexed citations breakdown →
20.
Siskind, Leah J., Tingxi Yu, Joseph S. Davis, et al.. (2008). Anti-apoptotic Bcl-2 Family Proteins Disassemble Ceramide Channels. Journal of Biological Chemistry. 283(11). 6622–6630. 108 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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