Michael H. Newberg

1.0k total citations
16 papers, 860 citations indexed

About

Michael H. Newberg is a scholar working on Immunology, Virology and Epidemiology. According to data from OpenAlex, Michael H. Newberg has authored 16 papers receiving a total of 860 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Immunology, 8 papers in Virology and 4 papers in Epidemiology. Recurrent topics in Michael H. Newberg's work include T-cell and B-cell Immunology (10 papers), Immune Cell Function and Interaction (9 papers) and HIV Research and Treatment (8 papers). Michael H. Newberg is often cited by papers focused on T-cell and B-cell Immunology (10 papers), Immune Cell Function and Interaction (9 papers) and HIV Research and Treatment (8 papers). Michael H. Newberg collaborates with scholars based in United States, Germany and Netherlands. Michael H. Newberg's co-authors include Norman L. Letvin, Víctor H. Engelhard, Dan H. Barouch, Darci A. Gorgone, Michelle A. Lifton, Gary J. Nabel, Dennis Panicali, Donna R. Vining, Maria Grazia Pau and Birgit Korioth-Schmitz and has published in prestigious journals such as The Journal of Experimental Medicine, The Journal of Immunology and Journal of Virology.

In The Last Decade

Michael H. Newberg

16 papers receiving 839 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael H. Newberg United States 16 509 307 253 224 207 16 860
Luis Mateo Australia 11 510 1.0× 417 1.4× 408 1.6× 98 0.4× 291 1.4× 12 949
Alp E. Oran United States 14 547 1.1× 219 0.7× 224 0.9× 67 0.3× 184 0.9× 15 860
Hans Hengartner Switzerland 14 908 1.8× 113 0.4× 218 0.9× 132 0.6× 214 1.0× 17 1.2k
Maureen F. Maughan United States 12 360 0.7× 226 0.7× 268 1.1× 141 0.6× 253 1.2× 17 840
Diane G. Carnathan United States 18 423 0.8× 379 1.2× 177 0.7× 124 0.6× 210 1.0× 33 817
Frédéric Delebecque France 13 532 1.0× 307 1.0× 315 1.2× 113 0.5× 430 2.1× 16 1.1k
David C. Diamond United States 13 282 0.6× 186 0.6× 291 1.2× 93 0.4× 108 0.5× 19 858
Donald K. Carter United States 9 603 1.2× 666 2.2× 293 1.2× 211 0.9× 327 1.6× 9 1.0k
Nathaniel L. Simmons United States 12 559 1.1× 599 2.0× 381 1.5× 237 1.1× 284 1.4× 14 1.1k
Don Siess United States 9 674 1.3× 588 1.9× 216 0.9× 113 0.5× 507 2.4× 13 1.2k

Countries citing papers authored by Michael H. Newberg

Since Specialization
Citations

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

Fields of papers citing papers by Michael H. Newberg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael H. Newberg. 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 Michael H. Newberg. The network helps show where Michael H. Newberg may publish in the future.

Co-authorship network of co-authors of Michael H. Newberg

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

All Works

16 of 16 papers shown
1.
Veazey, Ronald S., Susanne H.C. Baumeister, Melisa D. Rett, et al.. (2008). Increased Loss of CCR5 + CD45RA CD4 + T Cells in CD8 + Lymphocyte-Depleted Simian Immunodeficiency Virus-Infected Rhesus Monkeys. Journal of Virology. 82(11). 5618–5630. 30 indexed citations
2.
Kim, Eun‐Young, Ronald S. Veazey, Roland Zahn, et al.. (2008). Contribution of CD8+T Cells to Containment of Viral Replication and Emergence of Mutations inMamu-A*01-Restricted Epitopes in Simian Immunodeficiency Virus-Infected Rhesus Monkeys. Journal of Virology. 82(11). 5631–5635. 20 indexed citations
3.
Newberg, Michael H., Darci A. Gorgone, Michelle A. Lifton, et al.. (2006). Immunodomination in the Evolution of Dominant Epitope-Specific CD8+ T Lymphocyte Responses in Simian Immunodeficiency Virus-Infected Rhesus Monkeys. The Journal of Immunology. 176(1). 319–328. 33 indexed citations
4.
Schmitz, Jörn E., Darci A. Gorgone, Yue Sun, et al.. (2006). Preservation of Functional Virus-Specific Memory CD8+ T Lymphocytes in Vaccinated, Simian Human Immunodeficiency Virus-Infected Rhesus Monkeys. The Journal of Immunology. 176(9). 5338–5345. 29 indexed citations
5.
Seaman, Michael S., Sampa Santra, Michael H. Newberg, et al.. (2005). Vaccine-Elicited Memory Cytotoxic T Lymphocytes Contribute toMamu-A*01-Associated Control of Simian/Human Immunodeficiency Virus 89.6P Replication in Rhesus Monkeys. Journal of Virology. 79(8). 4580–4588. 22 indexed citations
6.
Barouch, Dan H., Maria Grazia Pau, Jerome Custers, et al.. (2004). Immunogenicity of Recombinant Adenovirus Serotype 35 Vaccine in the Presence of Pre-Existing Anti-Ad5 Immunity. The Journal of Immunology. 172(10). 6290–6297. 313 indexed citations
7.
Peyerl, Fred W., Heidi S. Bazick, Michael H. Newberg, et al.. (2004). Fitness Costs Limit Viral Escape from Cytotoxic T Lymphocytes at a Structurally Constrained Epitope. Journal of Virology. 78(24). 13901–13910. 81 indexed citations
8.
Newberg, Michael H., Marcelo J. Kuroda, William A. Charini, et al.. (2002). A Simian Immunodeficiency Virus Nef Peptide Is a Dominant Cytotoxic T Lymphocyte Epitope in Indian-Origin Rhesus Monkeys Expressing the Common MHC Class I Allele Mamu-A*02. Virology. 301(2). 365–373. 16 indexed citations
10.
Newberg, Michael H., John D. Jackson, James M. Hammel, et al.. (1996). USE OF GENE THERAPY TO SUPPRESS THE ANTIGEN-SPECIFIC IMMUNE RESPONSES IN MICE TO AN HLA ANTIGEN1. Transplantation. 62(6). 831–836. 20 indexed citations
11.
Newberg, Michael H., et al.. (1996). Importance of MHC class 1 α2 and α3 domains in the recognition of self and non-self MHC molecules. The Journal of Immunology. 156(7). 2473–2480. 84 indexed citations
12.
Man, Stephen, Michael H. Newberg, Victoria L. Crotzer, et al.. (1995). Definition of a human T cell epitope from influenza A non-structural protein 1 using HLA-A2.1 transgenic mice. International Immunology. 7(4). 597–605. 67 indexed citations
13.
Smith, David M., Jeffrey A. Bluestone, D. Rohan Jeyarajah, et al.. (1994). Inhibition of T cell activation by a monoclonal antibody reactive against the alpha 3 domain of human MHC class I molecules.. The Journal of Immunology. 153(3). 1054–1067. 49 indexed citations
14.
Newberg, Michael H., J P Ridge, Donna R. Vining, Russell D. Salter, & Víctor H. Engelhard. (1992). Species specificity in the interaction of CD8 with the alpha 3 domain of MHC class I molecules. The Journal of Immunology. 149(1). 136–142. 32 indexed citations
15.
McNiece, Ian, Michael H. Newberg, John A. Schetz, et al.. (1989). Detection and characterization of a B cell stimulatory factor (BSF-TC) derived from a bone marrow stromal cell line.. The Journal of Immunology. 142(11). 3894–3900. 16 indexed citations
16.
Purkerson, Jeffrey M., et al.. (1988). Interleukin 5 and interleukin 2 cooperate with interleukin 4 to induce IgG1 secretion from anti-Ig-treated B cells.. The Journal of Experimental Medicine. 168(3). 1175–1180. 23 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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