Douglas Padley

1.6k total citations
29 papers, 1.4k citations indexed

About

Douglas Padley is a scholar working on Immunology, Hematology and Oncology. According to data from OpenAlex, Douglas Padley has authored 29 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Immunology, 15 papers in Hematology and 11 papers in Oncology. Recurrent topics in Douglas Padley's work include Hematopoietic Stem Cell Transplantation (11 papers), Immunotherapy and Immune Responses (10 papers) and Immune Cell Function and Interaction (7 papers). Douglas Padley is often cited by papers focused on Hematopoietic Stem Cell Transplantation (11 papers), Immunotherapy and Immune Responses (10 papers) and Immune Cell Function and Interaction (7 papers). Douglas Padley collaborates with scholars based in United States. Douglas Padley's co-authors include Dennis A. Gastineau, Allan B. Dietz, Greg W. Butler, Svetomir N. Markovic, Luis F. Porrata, Stanimir Vuk‐Pavlović, Jozef Bartunek, André Terzic, Ruben Crespo‐Diaz and Michael G. Sarr and has published in prestigious journals such as Blood, Clinical Cancer Research and Clinical Pharmacology & Therapeutics.

In The Last Decade

Douglas Padley

28 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Douglas Padley United States 18 728 568 446 329 211 29 1.4k
Daria Pagliara Italy 21 685 0.9× 638 1.1× 678 1.5× 277 0.8× 352 1.7× 65 1.7k
Thomas Eiermann Germany 25 1.0k 1.4× 434 0.8× 361 0.8× 362 1.1× 286 1.4× 65 2.0k
Gianmaria Borleri Italy 22 1.1k 1.5× 1.1k 1.9× 523 1.2× 391 1.2× 467 2.2× 43 2.2k
S Samuel Israel 13 591 0.8× 324 0.6× 952 2.1× 147 0.4× 342 1.6× 28 1.4k
Ettore Biagi Italy 27 1.4k 1.9× 1.5k 2.7× 503 1.1× 658 2.0× 406 1.9× 75 2.7k
Udo Holtick Germany 25 631 0.9× 728 1.3× 546 1.2× 337 1.0× 322 1.5× 87 1.9k
Stacey Goodman United States 19 664 0.9× 613 1.1× 1.2k 2.7× 417 1.3× 277 1.3× 61 2.0k
Rainer Ordemann Germany 20 969 1.3× 444 0.8× 1.3k 3.0× 338 1.0× 607 2.9× 42 2.1k
HE Broxmeyer United States 17 611 0.8× 472 0.8× 707 1.6× 494 1.5× 670 3.2× 27 1.9k
Sabine Huenecke Germany 19 711 1.0× 504 0.9× 309 0.7× 197 0.6× 143 0.7× 53 1.2k

Countries citing papers authored by Douglas Padley

Since Specialization
Citations

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

Fields of papers citing papers by Douglas Padley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Douglas Padley

This figure shows the co-authorship network connecting the top 25 collaborators of Douglas Padley. A scholar is included among the top collaborators of Douglas Padley 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 Douglas Padley. Douglas Padley 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.
Knutson, Keith L., Matthew S. Block, Nadine Norton, et al.. (2019). Rapid Generation of Sustainable HER2-specific T-cell Immunity in Patients with HER2 Breast Cancer using a Degenerate HLA Class II Epitope Vaccine. Clinical Cancer Research. 26(5). 1045–1053. 11 indexed citations
2.
Kalli, Kimberly R., Matthew S. Block, Pashtoon Murtaza Kasi, et al.. (2018). Folate Receptor Alpha Peptide Vaccine Generates Immunity in Breast and Ovarian Cancer Patients. Clinical Cancer Research. 24(13). 3014–3025. 72 indexed citations
3.
Dietz, Allan B., Douglas Padley, Greg W. Butler, et al.. (2013). Data in support of the clinical use of adipose derived MSC: growth, storage, function and safety. Cytotherapy. 15(4). S5–S5.
4.
Lacy, Martha Q., Sumithra J. Mandrekar, Angela Dispenzieri, et al.. (2009). Idiotype‐pulsed antigen presenting cells following autologous transplantation for multiple myeloma may be associated with prolonged survival. American Journal of Hematology. 84(12). 799–802. 67 indexed citations
5.
Holtan, Shernan G., Luis F. Porrata, Ivana N. Micallef, et al.. (2007). AMD3100 Affects Autograft Lymphocyte Collection and Progression-Free Survival After Autologous Stem Cell Transplantion in Non-Hodgkin Lymphoma. Clinical Lymphoma & Myeloma. 7(4). 315–318. 49 indexed citations
6.
Dietz, Allan B., Douglas Padley, & Dennis A. Gastineau. (2007). Infrastructure Development for Human Cell Therapy Translation. Clinical Pharmacology & Therapeutics. 82(3). 320–324. 37 indexed citations
7.
Markovic, Svetomir N., Allan B. Dietz, Mary Maas, et al.. (2006). Preparing clinical-grade myeloid dendritic cells by electroporation-mediated transfection of in vitro amplified tumor-derived mRNA and safety testing in stage IV malignant melanoma. Journal of Translational Medicine. 4(1). 35–35. 24 indexed citations
8.
Litzow, Mark R., Allan B. Dietz, Peggy A. Bulur, et al.. (2006). Testing the safety of clinical-grade mature autologous myeloid DC in a phase I clinical immunotherapy trial of CML. Cytotherapy. 8(3). 290–298. 21 indexed citations
10.
Holtan, Shernan G., Luis F. Porrata, David J. Inwards, et al.. (2005). Effect of AMD3100 on T Lymphocyte Subpopulations in Apheresis Products of Patients Undergoing Autologous Hematopoietic Stem Cell Transplantation for Non Hodgkin Lymphoma.. Blood. 106(11). 2918–2918. 3 indexed citations
12.
Porrata, Luis F., Dennis A. Gastineau, Alvaro A. Pineda, et al.. (2004). Increasing the Number of Apheresis Collections Increases Lymphocyte Collection and Affects Survival after Autologous Stem Cell Transplantation for Non-Hodgkin Lymphoma.. Blood. 104(11). 892–892. 3 indexed citations
13.
Dietz, Allan B., Douglas Padley, Greg W. Butler, et al.. (2004). Clinical-grade manufacturing of DC from CD14+ precursors: experience from phase I clinical trials in CML and malignant melanoma. Cytotherapy. 6(6). 563–570. 20 indexed citations
14.
Porrata, Luis F., et al.. (2003). Re-infused Autologous Graft Natural Killer Cells Correlates with Absolute Lymphocyte Count Recovery after Autologous Stem Cell Transplantation. Leukemia & lymphoma. 44(6). 997–1000. 60 indexed citations
15.
Padley, Douglas, et al.. (2003). Endogenous microbial contamination of cultured autologous preparations in trials of cancer immunotherapy. Cytotherapy. 5(2). 147–152. 12 indexed citations
17.
Padley, Douglas, Allan B. Dietz, Dennis A. Gastineau, & Stanimir Vuk‐Pavlović. (2001). Mature Myeloid Dendritic Cells for Clinical Use Prepared from CD14 + Cells Isolated by Immunomagnetic Adsorption. Journal of Hematotherapy & Stem Cell Research. 10(3). 427–429. 17 indexed citations
18.
Padley, Douglas, Franklin P. Koontz, M Trigg, R. Gingrich, & R. G. Strauss. (1996). Bacterial contamination rates following processing of bone marrow and peripheral blood progenitor cell preparations. Transfusion. 36(1). 53–56. 32 indexed citations
20.
Trigg, ME, R. Gingrich, Nancy E. Goeken, et al.. (1989). Low rejection rate when using unrelated or haploidentical donors for children with leukemia undergoing marrow transplantation.. PubMed. 4(4). 431–7. 22 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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