Gisela Vaitaitis

1.0k total citations
30 papers, 770 citations indexed

About

Gisela Vaitaitis is a scholar working on Immunology, Genetics and Molecular Biology. According to data from OpenAlex, Gisela Vaitaitis has authored 30 papers receiving a total of 770 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Immunology, 12 papers in Genetics and 5 papers in Molecular Biology. Recurrent topics in Gisela Vaitaitis's work include T-cell and B-cell Immunology (20 papers), Immune Cell Function and Interaction (17 papers) and Diabetes and associated disorders (12 papers). Gisela Vaitaitis is often cited by papers focused on T-cell and B-cell Immunology (20 papers), Immune Cell Function and Interaction (17 papers) and Diabetes and associated disorders (12 papers). Gisela Vaitaitis collaborates with scholars based in United States, France and Croatia. Gisela Vaitaitis's co-authors include David H. Wagner, Richard J. Sanderson, Kathryn Haskins, Michelle Poulin, John E. Repine, Richard M. Wright, Nathan D. Pennock, Cathleen M. Dobbs, Thomas B. Repine and Lance S. Terada and has published in prestigious journals such as Proceedings of the National Academy of Sciences, The Journal of Immunology and PLoS ONE.

In The Last Decade

Gisela Vaitaitis

30 papers receiving 761 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gisela Vaitaitis United States 18 487 209 166 74 67 30 770
Amrita Nandan India 8 618 1.3× 98 0.5× 285 1.7× 29 0.4× 74 1.1× 12 946
Geming Lu United States 15 499 1.0× 65 0.3× 309 1.9× 20 0.3× 102 1.5× 28 898
Fuminori Hirano Japan 13 201 0.4× 61 0.3× 335 2.0× 39 0.5× 91 1.4× 23 742
Ying-Chun Lo United States 8 423 0.9× 41 0.2× 174 1.0× 16 0.2× 53 0.8× 9 717
Javier Torres‐Torronteras Spain 20 153 0.3× 117 0.6× 626 3.8× 26 0.4× 20 0.3× 36 1.0k
Masaji Kikukawa Japan 16 82 0.2× 254 1.2× 339 2.0× 145 2.0× 59 0.9× 27 727
P.A. Bäuerle Germany 5 490 1.0× 53 0.3× 214 1.3× 24 0.3× 36 0.5× 7 815
Xu Shi China 15 135 0.3× 64 0.3× 263 1.6× 34 0.5× 35 0.5× 32 677
Yunhai Luo China 16 122 0.3× 90 0.4× 654 3.9× 51 0.7× 89 1.3× 32 878
Liza D. Morales United States 12 190 0.4× 76 0.4× 406 2.4× 50 0.7× 40 0.6× 20 736

Countries citing papers authored by Gisela Vaitaitis

Since Specialization
Citations

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

Fields of papers citing papers by Gisela Vaitaitis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gisela Vaitaitis

This figure shows the co-authorship network connecting the top 25 collaborators of Gisela Vaitaitis. A scholar is included among the top collaborators of Gisela Vaitaitis 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 Gisela Vaitaitis. Gisela Vaitaitis 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.
2.
Vaitaitis, Gisela, et al.. (2022). Ocrevus reduces TH40 cells, a biomarker of systemic inflammation, in relapsing multiple sclerosis (RMS) and in progressive multiple sclerosis (PMS). Journal of Neuroimmunology. 374. 578008–578008. 4 indexed citations
3.
Vaitaitis, Gisela, et al.. (2019). Biomarker Discovery in Pre–Type 1 Diabetes; Th40 Cells as a Predictive Risk Factor. The Journal of Clinical Endocrinology & Metabolism. 104(9). 4127–4142. 7 indexed citations
4.
Vaitaitis, Gisela, Martin G. Yussman, & David H. Wagner. (2019). A CD40 targeting peptide prevents severe symptoms in experimental autoimmune encephalomyelitis. Journal of Neuroimmunology. 332. 8–15. 6 indexed citations
5.
Vaitaitis, Gisela, et al.. (2017). Th40 cells (CD4+CD40+ Tcells) drive a more severe form of Experimental Autoimmune Encephalomyelitis than conventional CD4 T cells. PLoS ONE. 12(2). e0172037–e0172037. 14 indexed citations
6.
Schreiner, Teri, et al.. (2014). Defining a new biomarker for the autoimmune component of Multiple Sclerosis: Th40 cells. Journal of Neuroimmunology. 270(1-2). 75–85. 17 indexed citations
7.
Vaitaitis, Gisela, et al.. (2014). A CD40-targeted peptide controls and reverses type 1 diabetes in NOD mice. Diabetologia. 57(11). 2366–2373. 26 indexed citations
8.
Vaitaitis, Gisela, et al.. (2013). An Alternative Role for Foxp3 As an Effector T Cell Regulator Controlled through CD40. The Journal of Immunology. 191(2). 717–725. 7 indexed citations
9.
Vaitaitis, Gisela & David H. Wagner. (2012). Galectin-9 Controls CD40 Signaling through a Tim-3 Independent Mechanism and Redirects the Cytokine Profile of Pathogenic T Cells in Autoimmunity. PLoS ONE. 7(6). e38708–e38708. 52 indexed citations
10.
Carter, Jessica, et al.. (2011). CD40 engagement of CD4+CD40+ T cells in a neo‐self antigen disease model ablates CTLA‐4 expression and indirectly impacts tolerance. European Journal of Immunology. 42(2). 424–435. 10 indexed citations
11.
Vaitaitis, Gisela & David H. Wagner. (2010). CD40 glycoforms and TNF-receptors 1 and 2 in the formation of CD40 receptor(s) in autoimmunity. Molecular Immunology. 47(14). 2303–2313. 17 indexed citations
12.
Vaitaitis, Gisela & David H. Wagner. (2008). High Distribution of CD40 and TRAF2 in Th40 T Cell Rafts Leads to Preferential Survival of this Auto-Aggressive Population in Autoimmunity. PLoS ONE. 3(4). e2076–e2076. 27 indexed citations
13.
Siebert, Janet, Margaret Inokuma, Nathan D. Pennock, et al.. (2007). An analytical workflow for investigating cytokine profiles. Cytometry Part A. 73A(4). 289–298. 22 indexed citations
14.
Wagner, R., Amy Putnam, Gisela Vaitaitis, et al.. (2007). A unique T cell subset described as CD4loCD40+ T cells (TCD40) in human type 1 diabetes. Clinical Immunology. 124(2). 138–148. 45 indexed citations
15.
Vaitaitis, Gisela, Michelle Poulin, Richard J. Sanderson, Kathryn Haskins, & David H. Wagner. (2003). Cutting Edge: CD40-Induced Expression of Recombination Activating Gene (RAG) 1 and RAG2: A Mechanism for the Generation of Autoaggressive T Cells in the Periphery. The Journal of Immunology. 170(7). 3455–3459. 49 indexed citations
16.
Wagner, David H., Gisela Vaitaitis, Richard J. Sanderson, et al.. (2002). Expression of CD40 identifies a unique pathogenic T cell population in type 1 diabetes. Proceedings of the National Academy of Sciences. 99(6). 3782–3787. 93 indexed citations
17.
Flores, S. C., Gisela Vaitaitis, Laura L. Coe, et al.. (2000). HIV-1 Tat increases endothelial solute permeability through tyrosine kinase and mitogen-activated protein kinase-dependent pathways. AIDS. 14(5). 475–482. 42 indexed citations
18.
Sève, Michel, Alain Favier, Mireille Osman, et al.. (1999). The Human Immunodeficiency Virus-1 Tat Protein Increases Cell Proliferation, Alters Sensitivity to Zinc Chelator-Induced Apoptosis, and Changes Sp1 DNA Binding in HeLa Cells. Archives of Biochemistry and Biophysics. 361(2). 165–172. 49 indexed citations
19.
20.
Wright, Richard M., et al.. (1995). Identification of the candidate ALS2 gene at chromosome 2q33 as a human aldehyde oxidase gene. Redox Report. 1(5). 313–321. 17 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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