Han Trinh

880 total citations · 1 hit paper
8 papers, 704 citations indexed

About

Han Trinh is a scholar working on Radiology, Nuclear Medicine and Imaging, Molecular Biology and Immunology. According to data from OpenAlex, Han Trinh has authored 8 papers receiving a total of 704 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Radiology, Nuclear Medicine and Imaging, 4 papers in Molecular Biology and 3 papers in Immunology. Recurrent topics in Han Trinh's work include Monoclonal and Polyclonal Antibodies Research (4 papers), CAR-T cell therapy research (2 papers) and Immune Response and Inflammation (2 papers). Han Trinh is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (4 papers), CAR-T cell therapy research (2 papers) and Immune Response and Inflammation (2 papers). Han Trinh collaborates with scholars based in United Kingdom, United States and Australia. Han Trinh's co-authors include John Ghrayeb, Bernard J. Scallon, David J. Shealy, J Vilček, Peter E. Daddona, Junming Le, David Knight, Scott A. Siegel, Margaret McDonough and Fionula M. Brennan and has published in prestigious journals such as Journal of Neurochemistry, Journal of Pharmacology and Experimental Therapeutics and Plant Molecular Biology.

In The Last Decade

Han Trinh

7 papers receiving 658 citations

Hit Papers

Construction and initial characterization of a mouse-huma... 1993 2026 2004 2015 1993 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
Han Trinh United Kingdom 6 271 205 201 143 131 8 704
Nancy Solowski United States 7 261 1.0× 205 1.0× 84 0.4× 124 0.9× 113 0.9× 11 699
C.–C. Chan United States 13 371 1.4× 265 1.3× 72 0.4× 167 1.2× 219 1.7× 19 1.3k
Rosemarie Watson Ireland 16 286 1.1× 471 2.3× 106 0.5× 66 0.5× 197 1.5× 27 1.1k
J. Woody Germany 6 320 1.2× 274 1.3× 85 0.4× 58 0.4× 96 0.7× 7 763
Laëtitia Le Pottier France 17 440 1.6× 169 0.8× 129 0.6× 39 0.3× 105 0.8× 33 893
Bi Zhou United States 15 583 2.2× 257 1.3× 236 1.2× 66 0.5× 188 1.4× 22 915
Howard Dang United States 17 393 1.5× 196 1.0× 122 0.6× 96 0.7× 226 1.7× 38 992
Kristi A. Koelsch United States 14 558 2.1× 291 1.4× 201 1.0× 70 0.5× 271 2.1× 26 1.1k
Satoshi Shiraishi Japan 15 197 0.7× 112 0.5× 64 0.3× 87 0.6× 203 1.5× 52 783
William D. Ratnoff United States 15 433 1.6× 124 0.6× 44 0.2× 92 0.6× 188 1.4× 18 777

Countries citing papers authored by Han Trinh

Since Specialization
Citations

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

Fields of papers citing papers by Han Trinh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Han Trinh

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

All Works

8 of 8 papers shown
1.
Trinh, Han, et al.. (2025). Artificial intelligence techniques in inherited retinal diseases: a review. Biomedical Physics & Engineering Express. 11(4). 42004–42004.
2.
Lenhard, Stephen C., Anthony Virtue, William Fieles, et al.. (2019). In Vivo Imaging of Small Molecular Weight Peptides for Targeted Renal Drug Delivery: A Study in Normal and Polycystic Kidney Diseased Mice. Journal of Pharmacology and Experimental Therapeutics. 370(3). 786–795. 9 indexed citations
3.
Holbrook, Joanna D., Catherine H. Gill, Noureddine Zebda, et al.. (2008). Characterisation of 5‐HT3C, 5‐HT3Dand 5‐HT3Ereceptor subunits: evolution, distribution and function. Journal of Neurochemistry. 108(2). 384–396. 64 indexed citations
4.
Corcoran, Anne E., Bernard J. Scallon, Han Trinh, et al.. (1998). Minimal tumor necrosis factor receptor binding protein: optimum biological activity of a truncated p55 soluble tumor necrosis factor receptor-IgG fusion protein.. PubMed. 9(3). 255–62. 3 indexed citations
5.
Scallon, Bernard J., Han Trinh, Mark Nedelman, et al.. (1995). Functional comparisons of different tumour necrosis factor receptor/IgG fusion proteins. Cytokine. 7(8). 759–770. 46 indexed citations
6.
Knight, David, Han Trinh, Junming Le, et al.. (1993). Construction and initial characterization of a mouse-human chimeric anti-TNF antibody. Molecular Immunology. 30(16). 1443–1453. 568 indexed citations breakdown →
7.
Looney, James E., David Knight, Han Trinh, et al.. (1992). High-level expression and characterization of a mouse-human chimeric CD4 antibody with therapeutic potential. Human Antibodies. 3(4). 191–200. 6 indexed citations
8.
Hastings, Craig, et al.. (1989). Molecular evolution of two actin genes from carrot. Plant Molecular Biology. 13(4). 375–383. 8 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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