Mo A. Dao

2.0k total citations · 1 hit paper
30 papers, 1.6k citations indexed

About

Mo A. Dao is a scholar working on Genetics, Molecular Biology and Oncology. According to data from OpenAlex, Mo A. Dao has authored 30 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Genetics, 14 papers in Molecular Biology and 10 papers in Oncology. Recurrent topics in Mo A. Dao's work include Virus-based gene therapy research (15 papers), Mesenchymal stem cell research (9 papers) and CAR-T cell therapy research (8 papers). Mo A. Dao is often cited by papers focused on Virus-based gene therapy research (15 papers), Mesenchymal stem cell research (9 papers) and CAR-T cell therapy research (8 papers). Mo A. Dao collaborates with scholars based in United States, Belgium and Japan. Mo A. Dao's co-authors include Jan A. Nolta, Daniel C. Link, Ivana Rosová, Ben Capoccia, Donald B. Kohn, Kimikazu Hashino, Itaru Kato, Naomi Taylor, Charles Hannum and Jesusa M.G. Arevalo and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Blood and The Journal of Immunology.

In The Last Decade

Mo A. Dao

29 papers receiving 1.6k citations

Hit Papers

Hypoxic Preconditioning Results in Increased Motility and... 2008 2026 2014 2020 2008 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
Mo A. Dao United States 19 767 734 455 429 350 30 1.6k
Stefania Piersanti Italy 13 1.1k 1.5× 765 1.0× 492 1.1× 215 0.5× 512 1.5× 16 2.1k
Sadafumi Suzuki Japan 16 799 1.0× 723 1.0× 352 0.8× 157 0.4× 367 1.0× 28 1.9k
R. E. Ploemacher Netherlands 17 766 1.0× 543 0.7× 642 1.4× 162 0.4× 324 0.9× 32 1.7k
Chantal Lechanteur Belgium 21 802 1.0× 410 0.6× 245 0.5× 191 0.4× 212 0.6× 50 1.4k
Stefano Michienzi Italy 6 1.1k 1.5× 595 0.8× 492 1.1× 94 0.2× 467 1.3× 6 1.9k
Alan F. Flint United States 10 940 1.2× 1.6k 2.1× 260 0.6× 266 0.6× 353 1.0× 12 2.5k
Marco Risso Italy 13 1.3k 1.7× 435 0.6× 421 0.9× 101 0.2× 341 1.0× 41 1.8k
Michael D. O’Hara United States 11 930 1.2× 853 1.2× 159 0.3× 214 0.5× 331 0.9× 20 1.9k
Robert C.H. Zhao China 18 1.3k 1.7× 608 0.8× 346 0.8× 92 0.2× 264 0.8× 21 1.9k
Marja Ekblom Sweden 26 761 1.0× 961 1.3× 1.3k 2.8× 201 0.5× 349 1.0× 55 2.9k

Countries citing papers authored by Mo A. Dao

Since Specialization
Citations

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

Fields of papers citing papers by Mo A. Dao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mo A. Dao. 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 Mo A. Dao. The network helps show where Mo A. Dao may publish in the future.

Co-authorship network of co-authors of Mo A. Dao

This figure shows the co-authorship network connecting the top 25 collaborators of Mo A. Dao. A scholar is included among the top collaborators of Mo A. Dao 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 Mo A. Dao. Mo A. Dao 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.
Hu, Xiaomeng, Mo A. Dao, Kathy White, et al.. (2021). Engineered Hypoimmune Allogeneic CAR T Cells Exhibit Innate and Adaptive Immune Evasion Even after Sensitization in Humanized Mice and Retain Potent Anti-Tumor Activity. Blood. 138(Supplement 1). 1690–1690. 3 indexed citations
2.
Dao, Mo A., Ciara C. Tate, Michael McGrogan, & Casey C. Case. (2013). Comparing the angiogenic potency of naïve marrow stromal cells and Notch-transfected marrow stromal cells. Journal of Translational Medicine. 11(1). 81–81. 31 indexed citations
3.
Dao, Mo A., Jan A. Nolta, & Casey C. Case. (2013). Immunosuppressive Activity of Adult Marrow Mesenchymal Stromal Cells on Innate Immune Cells in the Central Nervous System. 4(3). 177–185. 1 indexed citations
4.
Dao, Mo A., Ciara C. Tate, Irina Aizman, Michael McGrogan, & Casey C. Case. (2011). Comparing the immunosuppressive potency of naïve marrow stromal cells and Notch-transfected marrow stromal cells. Journal of Neuroinflammation. 8(1). 133–133. 19 indexed citations
5.
Bauer, Gerhard, Mo A. Dao, Scott S. Case, et al.. (2008). In Vivo Biosafety Model to Assess the Risk of Adverse Events From Retroviral and Lentiviral Vectors. Molecular Therapy. 16(7). 1308–1315. 56 indexed citations
6.
Rosová, Ivana, Mo A. Dao, Ben Capoccia, Daniel C. Link, & Jan A. Nolta. (2008). Hypoxic Preconditioning Results in Increased Motility and Improved Therapeutic Potential of Human Mesenchymal Stem Cells. Stem Cells. 26(8). 2173–2182. 560 indexed citations breakdown →
7.
Dao, Mo A., Michael H. Creer, Jan A. Nolta, & Catherine M. Verfaillie. (2007). Biology of umbilical cord blood progenitors in bone marrow niches. Blood. 110(1). 74–81. 38 indexed citations
9.
Dao, Mo A. & Jan A. Nolta. (2002). Retroviral-Mediated Transduction and Clonal Integration Analysis of Human Hematopoietic Stem and Progenitor Cells. Methods in molecular medicine. 63. 253–274. 1 indexed citations
10.
11.
Dao, Mo A., et al.. (2001). IL-7 Enhances the Responsiveness of Human T Cells That Develop in the Bone Marrow of Athymic Mice. The Journal of Immunology. 166(1). 170–181. 22 indexed citations
12.
Wang, Xiuli, et al.. (2001). Phenotypic Comparison of Extrathymic Human Bone-Marrow-Derived T Cells with Thymic-Selected T Cells Recovered from Different Tissues. Clinical Immunology. 100(3). 339–348. 3 indexed citations
13.
Dao, Mo A., et al.. (1999). Animal xenograft models for evaluation of gene transfer into human hematopoietic stem cells.. PubMed. 1(5). 553–7. 7 indexed citations
14.
Dao, Mo A. & Jan A. Nolta. (1999). Immunodeficient mice as models of human hematopoietic stem cell engraftment. Current Opinion in Immunology. 11(5). 532–537. 28 indexed citations
16.
Dao, Mo A. & Jan A. Nolta. (1998). Use of the bnx/hu xenograft model of human hematopoiesis to optimize methods for retroviral-mediated stem cell transduction (Review).. International Journal of Molecular Medicine. 1(1). 257–64. 24 indexed citations
17.
Dao, Mo A., Kimikazu Hashino, Itaru Kato, & Jan A. Nolta. (1998). Adhesion to Fibronectin Maintains Regenerative Capacity During Ex Vivo Culture and Transduction of Human Hematopoietic Stem and Progenitor Cells. Blood. 92(12). 4612–4621. 44 indexed citations
18.
Dao, Mo A., Karen Pepper, & Jan A. Nolta. (1997). Long‐Term Cytokine Production from Engineered Primary Human Stromal Cells Influences Human Hematopoiesis in an In Vivo Xenograft Model. Stem Cells. 15(6). 443–454. 53 indexed citations
19.
Dao, Mo A. & Jan A. Nolta. (1997). Inclusion of IL-3 during retrovirally-mediated transduction on stromal support does not increase the extent of gene transfer into long-term engrafting human hematopoietic progenitors.. PubMed. 3(2). 81–9. 10 indexed citations
20.
Dao, Mo A., Kimikazu Hashino, Lucília Kato, et al.. (1996). High efficiency transduction of human CD34' progenitors on fibronectin CH-296 verified by clonal integration analysis. Experimental Hematology. 24(9).

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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