Rolando Pajón

6.8k total citations · 2 hit papers
50 papers, 1.7k citations indexed

About

Rolando Pajón is a scholar working on Microbiology, Epidemiology and Infectious Diseases. According to data from OpenAlex, Rolando Pajón has authored 50 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Microbiology, 29 papers in Epidemiology and 15 papers in Infectious Diseases. Recurrent topics in Rolando Pajón's work include Bacterial Infections and Vaccines (30 papers), Pneumonia and Respiratory Infections (25 papers) and SARS-CoV-2 and COVID-19 Research (11 papers). Rolando Pajón is often cited by papers focused on Bacterial Infections and Vaccines (30 papers), Pneumonia and Respiratory Infections (25 papers) and SARS-CoV-2 and COVID-19 Research (11 papers). Rolando Pajón collaborates with scholars based in United States, Cuba and United Kingdom. Rolando Pajón's co-authors include Dan M. Granoff, Andrew Gorringe, Peter T. Beernink, Brett Leav, Hamilton Bennett, Laurence Chu, Biliana Nestorova, Wenmei Huang, Roderick McPhee and Daniel Yero and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Medicine and SHILAP Revista de lepidopterología.

In The Last Decade

Rolando Pajón

49 papers receiving 1.6k citations

Hit Papers

SARS-CoV-2 variant B.1.1.7 is susceptible to neutralizing... 2021 2026 2022 2024 2021 2023 50 100 150 200

Peers

Rolando Pajón
David Cooper United States
Igor Smolenov United States
Branda Hu United States
M Just Switzerland
Elizabeth Clutterbuck United Kingdom
David Cooper United States
Rolando Pajón
Citations per year, relative to Rolando Pajón Rolando Pajón (= 1×) peers David Cooper

Countries citing papers authored by Rolando Pajón

Since Specialization
Citations

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

Fields of papers citing papers by Rolando Pajón

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rolando Pajón

This figure shows the co-authorship network connecting the top 25 collaborators of Rolando Pajón. A scholar is included among the top collaborators of Rolando Pajón 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 Rolando Pajón. Rolando Pajón 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.
Chu, Laurence, Keith Vrbicky, David C. Montefiori, et al.. (2022). Immune response to SARS-CoV-2 after a booster of mRNA-1273: an open-label phase 2 trial. Nature Medicine. 28(5). 1042–1049. 56 indexed citations
2.
Jochum, Simon, et al.. (2022). Clinical Utility of Elecsys Anti-SARS-CoV-2 S Assay in COVID-19 Vaccination: An Exploratory Analysis of the mRNA-1273 Phase 1 Trial. Frontiers in Immunology. 12. 798117–798117. 39 indexed citations
3.
Huang, Yunda, Oleg Borisov, Jia Jin Kee, et al.. (2021). Calibration of two validated SARS-CoV-2 pseudovirus neutralization assays for COVID-19 vaccine evaluation. Scientific Reports. 11(1). 23921–23921. 20 indexed citations
4.
Shen, Xiaoying, Haili Tang, Charlene McDanal, et al.. (2021). SARS-CoV-2 variant B.1.1.7 is susceptible to neutralizing antibodies elicited by ancestral spike vaccines. Cell Host & Microbe. 29(4). 529–539.e3. 222 indexed citations breakdown →
5.
August, Allison, Husain Attarwala, Sunny Himansu, et al.. (2021). A phase 1 trial of lipid-encapsulated mRNA encoding a monoclonal antibody with neutralizing activity against Chikungunya virus. Nature Medicine. 27(12). 2224–2233. 118 indexed citations
6.
Rossi, Raffaella, et al.. (2015). A Mutant Library Approach to Identify Improved Meningococcal Factor H Binding Protein Vaccine Antigens. PLoS ONE. 10(6). e0128185–e0128185. 15 indexed citations
7.
Yero, Daniel, et al.. (2012). Predicted proteins of Neisseria meningitidis as potential vaccine candidates: from in silico analyses to experimental corroboration. SHILAP Revista de lepidopterología. 29(1). 22–28. 2 indexed citations
8.
Vu, David M., Rolando Pajón, Donald C. Reason, & Dan M. Granoff. (2012). A Broadly Cross-Reactive Monoclonal Antibody Against an Epitope on the N-terminus of Meningococcal fHbp. Scientific Reports. 2(1). 341–341. 33 indexed citations
9.
Beernink, Peter T., et al.. (2012). The Effect of Human Factor H on Immunogenicity of Meningococcal Native Outer Membrane Vesicle Vaccines with Over-Expressed Factor H Binding Protein. PLoS Pathogens. 8(5). e1002688–e1002688. 40 indexed citations
10.
Pajón, Rolando, et al.. (2011). Meningococcal Factor H Binding Proteins in Epidemic Strains from Africa: Implications for Vaccine Development. PLoS neglected tropical diseases. 5(9). e1302–e1302. 43 indexed citations
11.
Marsh, Jane W., Kathleen A. Shutt, Rolando Pajón, et al.. (2011). Diversity of factor H-binding protein in Neisseria meningitidis carriage isolates. Vaccine. 29(35). 6049–6058. 15 indexed citations
12.
Gil, Jeovanis, Lázaro Betancourt, Daniel Yero, et al.. (2009). Proteomic study via a non-gel based approach of meningococcal outer membrane vesicle vaccine obtained from strain CU385: A road map for discovering new antigens. Human Vaccines. 5(5). 347–356. 20 indexed citations
13.
Pajón, Rolando, Daniel Yero, Yanet Climent, et al.. (2009). Identification of new meningococcal serogroup B surface antigens through a systematic analysis of neisserial genomes. Vaccine. 28(2). 532–541. 19 indexed citations
14.
Yero, Daniel, et al.. (2007). Lipoprotein NMB0928 from Neisseria meningitidis serogroup B as a novel vaccine candidate. Vaccine. 25(50). 8420–8431. 29 indexed citations
15.
Findlow, Jamie, et al.. (2007). NeisseriaVaccines 2007. Expert Review of Vaccines. 6(4). 485–489. 10 indexed citations
16.
Pajón, Rolando, Daniel Yero, Andrey Pereira Lage, Alejandro Llanes, & Carlos Borroto. (2006). Computational identification of beta-barrel outer-membrane proteins in Mycobacterium tuberculosis predicted proteomes as putative vaccine candidates. Tuberculosis. 86(3-4). 290–302. 39 indexed citations
17.
Reddin, Karen M., et al.. (2005). Outer membrane vesicles of Neisseria lactamica as a potential mucosal adjuvant. Vaccine. 24(2). 206–214. 34 indexed citations
18.
González, Silvia A., et al.. (2001). Effect of P64k Presensitization on Its Efficacy as an Immunological Carrier in Mice. Biochemical and Biophysical Research Communications. 282(2). 376–379. 1 indexed citations
19.
Pajón, Rolando, et al.. (1998). Influence of the length of the P64k protein n-terminus stabilizing peptide on the expression in Escherichia coli of the fused TbpB from Neisseria meningitidis. Biotecnología aplicada. 15(4). 254–257. 2 indexed citations
20.
Pajón, Rolando, et al.. (1997). Sequence analysis of the structuraltbpAgene: protein topology and variable regions within neisserial receptors for transferrin iron acquisition. Microbial Pathogenesis. 23(2). 71–84. 13 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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