David J. Feith

2.9k total citations
90 papers, 2.0k citations indexed

About

David J. Feith is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, David J. Feith has authored 90 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 63 papers in Molecular Biology, 28 papers in Immunology and 24 papers in Oncology. Recurrent topics in David J. Feith's work include Polyamine Metabolism and Applications (28 papers), Sphingolipid Metabolism and Signaling (22 papers) and Amino Acid Enzymes and Metabolism (18 papers). David J. Feith is often cited by papers focused on Polyamine Metabolism and Applications (28 papers), Sphingolipid Metabolism and Signaling (22 papers) and Amino Acid Enzymes and Metabolism (18 papers). David J. Feith collaborates with scholars based in United States, Egypt and Spain. David J. Feith's co-authors include Anthony E. Pegg, Thomas P. Loughran, Lisa M. Shantz, Steven N. Steinway, Réka Albert, Jorge Gómez Tejeda Zañudo, Su‐Fern Tan, Louise Y.Y. Fong, Kristine C. Olson and André S. Bachmann and has published in prestigious journals such as Journal of Clinical Investigation, SHILAP Revista de lepidopterología and Blood.

In The Last Decade

David J. Feith

87 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David J. Feith United States 27 1.5k 418 337 334 262 90 2.0k
Andrea Sacchetti Netherlands 25 1.2k 0.8× 82 0.2× 249 0.7× 821 2.5× 93 0.4× 50 2.4k
Michel Raymondjean France 31 2.2k 1.5× 234 0.6× 228 0.7× 365 1.1× 55 0.2× 70 3.1k
Katrin Düvel United States 9 1.9k 1.3× 142 0.3× 316 0.9× 211 0.6× 82 0.3× 12 2.7k
Jeff L. Ellsworth United States 22 937 0.6× 169 0.4× 222 0.7× 166 0.5× 50 0.2× 42 2.1k
Mei Kong United States 25 2.2k 1.5× 158 0.4× 351 1.0× 558 1.7× 70 0.3× 42 3.4k
Jun Ishizaki Japan 21 1.1k 0.7× 63 0.2× 308 0.9× 208 0.6× 72 0.3× 49 1.9k
Anutosh Chakraborty United States 23 1.3k 0.9× 95 0.2× 313 0.9× 619 1.9× 151 0.6× 36 2.2k
Kirsteen J. Campbell United Kingdom 25 2.0k 1.3× 79 0.2× 561 1.7× 681 2.0× 147 0.6× 40 2.8k
Takao Isogai Japan 22 1.6k 1.1× 131 0.3× 192 0.6× 231 0.7× 60 0.2× 55 2.5k
Tania Maffucci United Kingdom 27 1.8k 1.2× 48 0.1× 193 0.6× 321 1.0× 91 0.3× 52 2.6k

Countries citing papers authored by David J. Feith

Since Specialization
Citations

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

Fields of papers citing papers by David J. Feith

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David J. Feith. 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 David J. Feith. The network helps show where David J. Feith may publish in the future.

Co-authorship network of co-authors of David J. Feith

This figure shows the co-authorship network connecting the top 25 collaborators of David J. Feith. A scholar is included among the top collaborators of David J. Feith 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 David J. Feith. David J. Feith 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.
Pastoret, Cédric, Jun Yang, David J. Feith, et al.. (2025). Diagnostic criteria for NK cell large granular lymphocyte leukemia: validation through a multicentric international study. Blood Advances. 10(3). 642–653.
2.
Pal, Ipsita, Anuradha Illendula, John S. Manavalan, et al.. (2025). Nanoromidepsin, a polymer nanoparticle of the HDAC inhibitor, improves safety and efficacy in models of T-cell lymphoma. Blood. 146(23). 2794–2807.
3.
Tsuboi, Kazuhito, Josefina Casas, Su‐Fern Tan, et al.. (2024). A fluorogenic substrate for the detection of lipid amidases in intact cells. Journal of Lipid Research. 65(3). 100520–100520. 1 indexed citations
4.
Olson, Kristine C., et al.. (2023). Anti-Inflammatory Response to Anti-Folate and Vitamin D Analog Combination in Large Granular Lymphocyte Leukemia. Blood. 142(Supplement 1). 4194–4194. 1 indexed citations
5.
Fisher‐Wellman, Kelsey H., Miki Kassai, P. Darrell Neufer, et al.. (2023). Simultaneous Inhibition of Ceramide Hydrolysis and Glycosylation Synergizes to Corrupt Mitochondrial Respiration and Signal Caspase Driven Cell Death in Drug-Resistant Acute Myeloid Leukemia. Cancers. 15(6). 1883–1883. 5 indexed citations
6.
Brammer, Jonathan E., Karen K. Ballen, Lubomir Sokol, et al.. (2023). Effective treatment with the selective cytokine inhibitor BNZ-1 reveals the cytokine dependency of T-LGL leukemia. Blood. 142(15). 1271–1280. 12 indexed citations
7.
O’Connor, Owen A., Anuradha Illendula, Kallesh D. Jayappa, et al.. (2023). Synthesis and Preclinical Development of a Promising Novel Romidepsin Nanoparticle for the Treatment of Peripheral T‐Cell Lymphoma (PTCL). Hematological Oncology. 41(S2). 548–548. 1 indexed citations
8.
Xing, Jeffrey C., Cait E. Hamele, Ross C. Hardison, et al.. (2022). Genomic landscape of TCRαβ and TCRγδ T-large granular lymphocyte leukemia. Blood. 139(20). 3058–3072. 37 indexed citations
9.
Olson, Thomas L., Jeffrey C. Xing, Kristine C. Olson, et al.. (2021). Frequent somatic TET2 mutations in chronic NK-LGL leukemia with distinct patterns of cytopenias. Blood. 138(8). 662–673. 33 indexed citations
10.
11.
Pearson, Jennifer M., Su‐Fern Tan, Arati Sharma, et al.. (2019). Ceramide Analogue SACLAC Modulates Sphingolipid Levels and MCL-1 Splicing to Induce Apoptosis in Acute Myeloid Leukemia. Molecular Cancer Research. 18(3). 352–363. 23 indexed citations
12.
Yang, Jun, Francis LeBlanc, Cait E. Hamele, et al.. (2018). TRAIL mediates and sustains constitutive NF-κB activation in LGL leukemia. Blood. 131(25). 2803–2815. 32 indexed citations
13.
Olson, Kristine C., et al.. (2018). Dysregulation of the IFN-γ-STAT1 signaling pathway in a cell line model of large granular lymphocyte leukemia. PLoS ONE. 13(2). e0193429–e0193429. 15 indexed citations
14.
LeBlanc, Francis, Xin Liu, Jeremy A. Hengst, et al.. (2015). Sphingosine kinase inhibitors decrease viability and induce cell death in natural killer-large granular lymphocyte leukemia. Cancer Biology & Therapy. 16(12). 1830–1840. 23 indexed citations
15.
Steinway, Steven N., et al.. (2015). Combinatorial interventions inhibit TGFβ-driven epithelial-to-mesenchymal transition and support hybrid cellular phenotypes. npj Systems Biology and Applications. 1(1). 15014–15014. 101 indexed citations
16.
Steinway, Steven N., Jorge Gómez Tejeda Zañudo, Wei Ding, et al.. (2014). Network Modeling of TGFβ Signaling in Hepatocellular Carcinoma Epithelial-to-Mesenchymal Transition Reveals Joint Sonic Hedgehog and Wnt Pathway Activation. Cancer Research. 74(21). 5963–5977. 188 indexed citations
17.
Feith, David J.. (2011). Carcinogenesis Studies in Mice with Genetically Engineered Alterations in Polyamine Metabolism. Methods in molecular biology. 720. 129–141. 3 indexed citations
18.
19.
Feith, David J., et al.. (2007). Mouse skin chemical carcinogenesis is inhibited by antizyme in promotion‐sensitive and promotion‐resistant genetic backgrounds. Molecular Carcinogenesis. 46(6). 453–465. 16 indexed citations
20.
Feith, David J., Louise Y.Y. Fong, & Anthony E. Pegg. (2005). Antizyme inhibits N-nitrosomethylbenzylamine-induced mouse forestomach carcinogenesis in a p53-independent manner. Cancer Research. 65. 915–916. 1 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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