Daming Shan

1.2k total citations
9 papers, 974 citations indexed

About

Daming Shan is a scholar working on Radiology, Nuclear Medicine and Imaging, Molecular Biology and Oncology. According to data from OpenAlex, Daming Shan has authored 9 papers receiving a total of 974 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Molecular Biology and 5 papers in Oncology. Recurrent topics in Daming Shan's work include Monoclonal and Polyclonal Antibodies Research (7 papers), CAR-T cell therapy research (5 papers) and Immunotherapy and Immune Responses (4 papers). Daming Shan is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (7 papers), CAR-T cell therapy research (5 papers) and Immunotherapy and Immune Responses (4 papers). Daming Shan collaborates with scholars based in United States, China and South Africa. Daming Shan's co-authors include Oliver W. Press, Jeffrey A. Ledbetter, Theta T. Tsu, Ajay K. Gopal, Hongzhi Li, Kang Yu, Kaiyan Yang, Panyuan Li, Yuting Huang and Haihua Gu and has published in prestigious journals such as Blood, The Journal of Immunology and Frontiers in Oncology.

In The Last Decade

Daming Shan

9 papers receiving 950 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Daming Shan United States	9	507	398	366	364	292	9	974
Zhengxing Qu United States	13	465 0.9×	200 0.5×	336 0.9×	261 0.7×	230 0.8×	17	822
Julie A. McEarchern United States	13	294 0.6×	189 0.5×	537 1.5×	384 1.1×	387 1.3×	25	1.0k
Waleed Alduaij Canada	9	270 0.5×	545 1.4×	348 1.0×	263 0.7×	155 0.5×	21	942
Margit Schmidt Germany	7	427 0.8×	211 0.5×	715 2.0×	384 1.1×	232 0.8×	13	1.0k
Heather Horne United States	11	389 0.8×	403 1.0×	355 1.0×	198 0.5×	102 0.3×	31	795
Steven I. Park United States	18	268 0.5×	629 1.6×	571 1.6×	153 0.4×	160 0.5×	65	1.1k
C. Ian Mockridge United Kingdom	28	874 1.7×	530 1.3×	523 1.4×	1.3k 3.5×	590 2.0×	43	2.1k
Sylvia Herter Switzerland	11	345 0.7×	105 0.3×	369 1.0×	332 0.9×	236 0.8×	35	765
Stefan Knackmuss Germany	23	640 1.3×	168 0.4×	660 1.8×	485 1.3×	534 1.8×	41	1.4k
Ralf Lutterbüse Germany	11	580 1.1×	102 0.3×	681 1.9×	447 1.2×	304 1.0×	13	1.0k

Countries citing papers authored by Daming Shan

Since Specialization

Citations

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

Fields of papers citing papers by Daming Shan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daming Shan

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

All Works

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9 of 9 papers shown

Li, Panyuan, Yuting Huang, Kaiyan Yang, et al.. (2020). The Third Generation Anti-HER2 Chimeric Antigen Receptor Mouse T Cells Alone or Together With Anti-PD1 Antibody Inhibits the Growth of Mouse Breast Tumor Cells Expressing HER2 in vitro and in Immune Competent Mice. Frontiers in Oncology. 10. 1143–1143. 34 indexed citations

Hu, Wangxiong, et al.. (2014). Adoptive Antitumor Immunotherapy In Vitro and In Vivo Using Genetically Activated erbB2-specific T Cells. Journal of Immunotherapy. 37(7). 351–359. 12 indexed citations

Hu, Wangxiong, et al.. (2012). Gene Therapy of Malignant Solid Tumors by Targeting erbB2 Receptors and by Activating T Cells. Cancer Biotherapy and Radiopharmaceuticals. 27(10). 711–718. 10 indexed citations

Yu, Kang, et al.. (2008). Immunotherapy of lymphomas with T cells modified by anti-CD20 scFv/CD28/CD3ζ recombinant gene. Leukemia & lymphoma. 49(7). 1368–1373. 16 indexed citations

Shan, Daming, Ajay K. Gopal, & Oliver W. Press. (2001). Synergistic effects of the fenretinide (4-HPR) and anti-CD20 monoclonal antibodies on apoptosis induction of malignant human B cells.. PubMed. 7(8). 2490–5. 23 indexed citations

Shan, Daming, Jeffrey A. Ledbetter, & Oliver W. Press. (2000). Signaling events involved in anti-CD20-induced apoptosis of malignant human B cells. Cancer Immunology Immunotherapy. 48(12). 673–683. 316 indexed citations

Shan, Daming, et al.. (1999). Characterization of scFv-Ig Constructs Generated from the Anti-CD20 mAb 1F5 Using Linker Peptides of Varying Lengths. The Journal of Immunology. 162(11). 6589–6595. 53 indexed citations

Shan, Daming, Jeffrey A. Ledbetter, & Oliver W. Press. (1998). Apoptosis of Malignant Human B Cells by Ligation of CD20 With Monoclonal Antibodies. Blood. 91(5). 1644–1652. 478 indexed citations

Shan, Daming, Jeffrey A. Ledbetter, & Oliver W. Press. (1998). Apoptosis of Malignant Human B Cells by Ligation of CD20 With Monoclonal Antibodies. Blood. 91(5). 1644–1652. 32 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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