Deborah M. Brown

3.5k total citations
71 papers, 2.8k citations indexed

About

Deborah M. Brown is a scholar working on Immunology, Epidemiology and Molecular Biology. According to data from OpenAlex, Deborah M. Brown has authored 71 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Immunology, 14 papers in Epidemiology and 8 papers in Molecular Biology. Recurrent topics in Deborah M. Brown's work include Immune Cell Function and Interaction (24 papers), Immunotherapy and Immune Responses (21 papers) and T-cell and B-cell Immunology (17 papers). Deborah M. Brown is often cited by papers focused on Immune Cell Function and Interaction (24 papers), Immunotherapy and Immune Responses (21 papers) and T-cell and B-cell Immunology (17 papers). Deborah M. Brown collaborates with scholars based in United States, Netherlands and Canada. Deborah M. Brown's co-authors include Susan L. Swain, Richard P. Phipps, Rachel L. Roper, Dawn M. Jelley‐Gibbs, Eulogia Román, María de la Luz García-Hernández, Sarah Lee, Tara M. Strutt, K. Kai McKinstry and John Dibble and has published in prestigious journals such as Journal of Biological Chemistry, Journal of Clinical Investigation and The Journal of Experimental Medicine.

In The Last Decade

Deborah M. Brown

70 papers receiving 2.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Deborah M. Brown United States 27 1.8k 875 519 315 261 71 2.8k
Emmanuelle Faure United States 12 2.3k 1.3× 612 0.7× 960 1.8× 200 0.6× 240 0.9× 16 3.5k
Maria Regina D’Império Lima Brazil 32 1.3k 0.7× 1.0k 1.1× 601 1.2× 112 0.4× 298 1.1× 89 2.9k
Karina Ramalho Bortoluci Brazil 21 1.4k 0.8× 600 0.7× 1.0k 2.0× 197 0.6× 245 0.9× 47 2.9k
Mohammad Kazemi Arababadi Iran 30 1.1k 0.6× 1.2k 1.4× 528 1.0× 354 1.1× 207 0.8× 189 3.1k
Jean Gosselin Canada 34 1.5k 0.8× 1.0k 1.2× 617 1.2× 894 2.8× 341 1.3× 67 3.0k
Carlos del Fresno Spain 30 1.9k 1.1× 510 0.6× 688 1.3× 336 1.1× 393 1.5× 60 3.0k
Miwa Sasai Japan 27 2.1k 1.1× 1.5k 1.7× 1.3k 2.5× 196 0.6× 281 1.1× 61 3.8k
Pascale Kropf United Kingdom 33 1.9k 1.0× 1.4k 1.6× 632 1.2× 289 0.9× 304 1.2× 68 4.2k
Marcela Rosas United Kingdom 25 1.9k 1.0× 856 1.0× 989 1.9× 192 0.6× 838 3.2× 33 3.6k

Countries citing papers authored by Deborah M. Brown

Since Specialization
Citations

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

Fields of papers citing papers by Deborah M. Brown

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Deborah M. Brown. 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 Deborah M. Brown. The network helps show where Deborah M. Brown may publish in the future.

Co-authorship network of co-authors of Deborah M. Brown

This figure shows the co-authorship network connecting the top 25 collaborators of Deborah M. Brown. A scholar is included among the top collaborators of Deborah M. Brown 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 Deborah M. Brown. Deborah M. Brown 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.
Roberts, Alan D., et al.. (2023). Resuspension of Seeded Particles Containing Live Influenza A Virus in a Full-Scale Laboratory. Buildings. 13(7). 1734–1734. 6 indexed citations
2.
Ferro, Andrea R., Brian T. Helenbrook, Goodarz Ahmadi, et al.. (2022). Characterizing respiratory aerosol emissions during sustained phonation. Journal of Exposure Science & Environmental Epidemiology. 32(5). 689–696. 10 indexed citations
3.
Stec, Jozef, et al.. (2020). Analysis of Student Perceptions of Just-In-Time Teaching Pedagogy in PharmD Microbiology and Immunology Courses. Frontiers in Immunology. 11. 351–351. 8 indexed citations
4.
Puniya, Bhanwar Lal, et al.. (2018). A Mechanistic Computational Model Reveals That Plasticity of CD4+ T Cell Differentiation Is a Function of Cytokine Composition and Dosage. Frontiers in Physiology. 9. 878–878. 31 indexed citations
5.
Brown, Deborah M., et al.. (2018). Oral non-viral gene delivery for applications in DNA vaccination and gene therapy. Current Opinion in Biomedical Engineering. 7. 51–57. 19 indexed citations
6.
Brown, Deborah M., et al.. (2017). Chitosan-zein nano-in-microparticles capable of mediating in vivo transgene expression following oral delivery. Journal of Controlled Release. 249. 150–161. 58 indexed citations
7.
Brown, Deborah M., et al.. (2016). To flip or not to flip: an evaluation of the flipped classroom approach for psychiatry trainees. eCite Digital Repository (University of Tasmania). 1 indexed citations
8.
Brown, Deborah M., et al.. (2016). Significant role for IRF3 in both T cell and APC effector functions during T cell responses. Cellular Immunology. 310. 141–149. 22 indexed citations
9.
Brown, Deborah M., et al.. (2015). Single-Dose CpG Immunization Protects Against a Heterosubtypic Challenge and Generates Antigen-Specific Memory T Cells. Frontiers in Immunology. 6. 327–327. 9 indexed citations
10.
Harris, Seth, et al.. (2014). Early Cytokine Dysregulation and Viral Replication Are Associated with Mortality During Lethal Influenza Infection. Viral Immunology. 27(5). 214–224. 34 indexed citations
11.
Workman, Aspen M., et al.. (2014). Inflammation Enhances IL-2 Driven Differentiation of Cytolytic CD4 T Cells. PLoS ONE. 9(2). e89010–e89010. 41 indexed citations
12.
Cody, Liz A., et al.. (2013). IRF3 helps control acute TMEV infection through IL-6 expression but contributes to acute hippocampus damage following TMEV infection. Virus Research. 178(2). 226–233. 19 indexed citations
13.
Brown, Deborah M., et al.. (2013). Interferon response factor 3 is crucial to poly-I:C induced NK cell activity and control of B16 melanoma growth. Cancer Letters. 346(1). 122–128. 20 indexed citations
14.
McKinstry, K. Kai, Tara M. Strutt, Yi Kuang, et al.. (2012). Memory CD4+ T cells protect against influenza through multiple synergizing mechanisms. Journal of Clinical Investigation. 122(8). 2847–2856. 180 indexed citations
15.
Alsalleeh, Fahd, et al.. (2011). IRF3 polymorphisms induce different innate anti-Theiler's virus immune responses in RAW264.7 macrophages. Virology. 418(1). 40–48. 15 indexed citations
16.
Thompson, Kim D., et al.. (2010). Sound Intensity and Noise Evaluation in a Critical Care Unit. American Journal of Critical Care. 19(6). e88–e98. 80 indexed citations
17.
Brown, Deborah M., et al.. (2006). CD4 T Cell-Mediated Protection from Lethal Influenza: Perforin and Antibody-Mediated Mechanisms Give a One-Two Punch. The Journal of Immunology. 177(5). 2888–2898. 234 indexed citations
18.
Brown, Deborah M., et al.. (2001). Tumours can act as adjuvants for humoral immunity. Immunology. 102(4). 486–497. 72 indexed citations
19.
Fedyk, Eric R., Deborah M. Brown, & Richard P. Phipps. (1997). PGE2 Regulation of B Lymphocytes and T Helper 1 and T Helper 2 Cells: Induction of Inflammatory versus Allergic Responses. Advances in experimental medicine and biology. 407. 237–242. 18 indexed citations
20.
Fedyk, Eric R., Melinda A. Borrello, Deborah M. Brown, & Richard P. Phipps. (1994). Regulation of B Cell Tolerance and Triggering by Immune Complexes. Chemical immunology/Fortschritte der Allergielehre/Progress in allergy/Chemical immunology and allergy. 58. 67–91. 5 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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