Christopher Tweed

1.1k total citations · 1 hit paper
7 papers, 880 citations indexed

About

Christopher Tweed is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cancer Research. According to data from OpenAlex, Christopher Tweed has authored 7 papers receiving a total of 880 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 4 papers in Cellular and Molecular Neuroscience and 2 papers in Cancer Research. Recurrent topics in Christopher Tweed's work include Nerve injury and regeneration (4 papers), TGF-β signaling in diseases (2 papers) and Pancreatic and Hepatic Oncology Research (1 paper). Christopher Tweed is often cited by papers focused on Nerve injury and regeneration (4 papers), TGF-β signaling in diseases (2 papers) and Pancreatic and Hepatic Oncology Research (1 paper). Christopher Tweed collaborates with scholars based in United Kingdom, United States and Canada. Christopher Tweed's co-authors include Nicholas J. Laping, Eugene T. Grygielko, John D. Harling, W. Martin, Jennifer M. Bomberger, Barbara A. Olson, James A. Fornwald, Laramie M. Gaster, Ruth Lehr and James F. Callahan and has published in prestigious journals such as Journal of Neuroscience, Brain Research and Journal of Pharmacology and Experimental Therapeutics.

In The Last Decade

Christopher Tweed

6 papers receiving 868 citations

Hit Papers

Inhibition of Transforming Growth Factor (TGF)-β1–Induced... 2002 2026 2010 2018 2002 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Christopher Tweed United Kingdom 6 524 150 108 82 80 7 880
Arindel S.R. Maharaj United States 10 761 1.5× 108 0.7× 104 1.0× 77 0.9× 80 1.0× 19 1.5k
Juan A. Ardura Spain 19 687 1.3× 287 1.9× 129 1.2× 100 1.2× 86 1.1× 35 1.1k
Maureen A. McDonnell United States 7 881 1.7× 270 1.8× 52 0.5× 70 0.9× 39 0.5× 8 1.2k
Andreas Herrlich United States 20 693 1.3× 267 1.8× 122 1.1× 101 1.2× 56 0.7× 30 1.3k
Jaeryung Kim South Korea 19 491 0.9× 173 1.2× 47 0.4× 73 0.9× 60 0.8× 38 1.4k
Atsushi Suzuki Japan 18 726 1.4× 258 1.7× 67 0.6× 132 1.6× 104 1.3× 36 1.2k
Huiming Xu China 20 589 1.1× 99 0.7× 101 0.9× 110 1.3× 46 0.6× 56 1.1k
Young Shin Ryu South Korea 10 628 1.2× 97 0.6× 101 0.9× 60 0.7× 60 0.8× 14 869
Hideaki Tanaka Japan 15 344 0.7× 139 0.9× 206 1.9× 114 1.4× 60 0.8× 39 995
Noriko Mizusawa Japan 21 440 0.8× 143 1.0× 36 0.3× 173 2.1× 57 0.7× 41 999

Countries citing papers authored by Christopher Tweed

Since Specialization
Citations

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

Fields of papers citing papers by Christopher Tweed

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Christopher Tweed

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

All Works

7 of 7 papers shown
1.
Grisham, Rachel N., Isabelle Laure Ray-Coquard, Charles Anderson, et al.. (2025). 68O Blood ctDNA vs tumor tissue screening for the detection of KRAS mutations in low-grade serous ovarian cancer. ESMO Open. 10. 105198–105198.
3.
Smith, Darrell R., Christopher Tweed, Paul Fernyhough, & Gordon W. Glazner. (2009). Nuclear Factor-κB Activation in Axons and Schwann Cells in Experimental Sciatic Nerve Injury and Its Role in Modulating Axon Regeneration: Studies With Etanercept. Journal of Neuropathology & Experimental Neurology. 68(6). 691–700. 35 indexed citations
4.
Grygielko, Eugene T., W. Martin, Christopher Tweed, et al.. (2005). Inhibition of Gene Markers of Fibrosis with a Novel Inhibitor of Transforming Growth Factor-β Type I Receptor Kinase in Puromycin-Induced Nephritis. Journal of Pharmacology and Experimental Therapeutics. 313(3). 943–951. 153 indexed citations
5.
Fernyhough, Paul, Darrell R. Smith, Jason Schapansky, et al.. (2005). Activation of Nuclear Factor-κB via Endogenous Tumor Necrosis Factor α Regulates Survival of Axotomized Adult Sensory Neurons. Journal of Neuroscience. 25(7). 1682–1690. 78 indexed citations
6.
Dluzen, Dean E., Christopher Tweed, Linda I. Anderson, & Nicholas J. Laping. (2003). Gender Differences in Methamphetamine-Induced mRNA Associated with Neurodegeneration in the Mouse Nigrostriatal Dopaminergic System. Neuroendocrinology. 77(4). 232–238. 39 indexed citations
7.
Laping, Nicholas J., Eugene T. Grygielko, Anil Mathur, et al.. (2002). Inhibition of Transforming Growth Factor (TGF)-β1–Induced Extracellular Matrix with a Novel Inhibitor of the TGF-β Type I Receptor Kinase Activity: SB-431542. Molecular Pharmacology. 62(1). 58–64. 544 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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