Juan J. Martinez

1.4k total citations
25 papers, 998 citations indexed

About

Juan J. Martinez is a scholar working on Parasitology, Public Health, Environmental and Occupational Health and Infectious Diseases. According to data from OpenAlex, Juan J. Martinez has authored 25 papers receiving a total of 998 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Parasitology, 11 papers in Public Health, Environmental and Occupational Health and 8 papers in Infectious Diseases. Recurrent topics in Juan J. Martinez's work include Vector-borne infectious diseases (22 papers), Mosquito-borne diseases and control (10 papers) and Viral Infections and Vectors (6 papers). Juan J. Martinez is often cited by papers focused on Vector-borne infectious diseases (22 papers), Mosquito-borne diseases and control (10 papers) and Viral Infections and Vectors (6 papers). Juan J. Martinez collaborates with scholars based in United States, Portugal and France. Juan J. Martinez's co-authors include Sean P. Riley, Marissa M. Cardwell, Yvonne G. Y. Chan, Esteban Veiga, Pascale Cossart, Stéphanie Seveau, Shigemi Matsuyama, Tsuneo Uchiyama, Isaura Simões and Jennifer Patterson and has published in prestigious journals such as Cell, PLoS ONE and Nature Reviews Microbiology.

In The Last Decade

Juan J. Martinez

24 papers receiving 991 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Juan J. Martinez United States 17 622 329 227 209 197 25 998
Wolfram R. Zückert United States 21 843 1.4× 165 0.5× 257 1.1× 302 1.4× 532 2.7× 38 1.2k
Magda Beier‐Sexton United States 13 404 0.6× 206 0.6× 181 0.8× 207 1.0× 177 0.9× 13 779
Hua Niu China 18 338 0.5× 182 0.6× 287 1.3× 110 0.5× 214 1.1× 38 994
Álvaro Toledo United States 20 763 1.2× 185 0.6× 312 1.4× 206 1.0× 588 3.0× 38 1.2k
Jeffrey G. Shannon United States 16 393 0.6× 390 1.2× 230 1.0× 61 0.3× 327 1.7× 20 1.0k
Ik-Sang Kim South Korea 22 941 1.5× 432 1.3× 225 1.0× 103 0.5× 508 2.6× 31 1.3k
Anja Lührmann Germany 17 641 1.0× 390 1.2× 379 1.7× 133 0.6× 338 1.7× 38 1.4k
Magda S. Beier United States 15 458 0.7× 737 2.2× 199 0.9× 267 1.3× 230 1.2× 22 1.2k
Naotoshi Tsuji Japan 24 869 1.4× 177 0.5× 406 1.8× 246 1.2× 239 1.2× 68 1.6k
Damon W. Ellison United States 16 302 0.5× 346 1.1× 337 1.5× 126 0.6× 378 1.9× 43 1.2k

Countries citing papers authored by Juan J. Martinez

Since Specialization
Citations

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

Fields of papers citing papers by Juan J. Martinez

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Juan J. Martinez. 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 Juan J. Martinez. The network helps show where Juan J. Martinez may publish in the future.

Co-authorship network of co-authors of Juan J. Martinez

This figure shows the co-authorship network connecting the top 25 collaborators of Juan J. Martinez. A scholar is included among the top collaborators of Juan J. Martinez 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 Juan J. Martinez. Juan J. Martinez 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.
Noland, Robert C., et al.. (2021). Rickettsia conorii survival in THP ‐1 macrophages involves host lipid droplet alterations and active rickettsial protein production. Cellular Microbiology. 23(11). e13390–e13390. 7 indexed citations
2.
3.
Martinez, Juan J., et al.. (2020). Modulation of Host Lipid Pathways by Pathogenic Intracellular Bacteria. Pathogens. 9(8). 614–614. 19 indexed citations
4.
Riley, Sean P., et al.. (2019). Macrophages Infected by a Pathogen and a Non-pathogen Spotted Fever Group Rickettsia Reveal Differential Reprogramming Signatures Early in Infection. Frontiers in Cellular and Infection Microbiology. 9. 97–97. 26 indexed citations
5.
Santa, Cátia, et al.. (2019). A Pathogen and a Non-pathogen Spotted Fever Group Rickettsia Trigger Differential Proteome Signatures in Macrophages. Frontiers in Cellular and Infection Microbiology. 9. 43–43. 21 indexed citations
6.
7.
Riley, Sean P., et al.. (2017). The Rickettsia conorii Adr1 Interacts with the C-Terminus of Human Vitronectin in a Salt-Sensitive Manner. Frontiers in Cellular and Infection Microbiology. 7. 61–61. 11 indexed citations
8.
McClure, Erin E., Adela S. Oliva Chávez, Dana K. Shaw, et al.. (2017). Engineering of obligate intracellular bacteria: progress, challenges and paradigms. Nature Reviews Microbiology. 15(9). 544–558. 121 indexed citations
9.
Simões, Isaura, et al.. (2016). Differences in Intracellular Fate of Two Spotted Fever Group Rickettsia in Macrophage-Like Cells. Frontiers in Cellular and Infection Microbiology. 6. 80–80. 37 indexed citations
10.
11.
Huesgen, Pitter F., Sean P. Riley, Alexander Wlodawer, et al.. (2014). RC1339/APRc from Rickettsia conorii Is a Novel Aspartic Protease with Properties of Retropepsin-Like Enzymes. PLoS Pathogens. 10(8). e1004324–e1004324. 15 indexed citations
12.
Czyż, Daniel M., Lakshmi‐Prasad Potluri, Sean P. Riley, et al.. (2014). Host-Directed Antimicrobial Drugs with Broad-Spectrum Efficacy against Intracellular Bacterial Pathogens. mBio. 5(4). e01534–14. 68 indexed citations
13.
Cardwell, Marissa M. & Juan J. Martinez. (2012). Identification and characterization of the mammalian association and actin-nucleating domains in theRickettsia conoriiautotransporter protein, Sca2. Cellular Microbiology. 14(9). 1485–1495. 19 indexed citations
14.
Riley, Sean P., Jennifer Patterson, & Juan J. Martinez. (2012). The Rickettsial OmpB β-Peptide of Rickettsia conorii Is Sufficient To Facilitate Factor H-Mediated Serum Resistance. Infection and Immunity. 80(8). 2735–2743. 24 indexed citations
15.
Martinez, Juan J., et al.. (2012). OmpA-mediated rickettsial adherence to and invasion of human endothelial cells is dependent upon interaction with α2β1 integrin. Cellular Microbiology. 15(5). 727–741. 57 indexed citations
16.
Riley, Sean P., et al.. (2010). TheRickettsia conoriiAutotransporter Protein Sca1 Promotes Adherence to Nonphagocytic Mammalian Cells. Infection and Immunity. 78(5). 1895–1904. 60 indexed citations
17.
Chan, Yvonne G. Y., Sean P. Riley, & Juan J. Martinez. (2010). Adherence to and Invasion of Host Cells by Spotted Fever Group Rickettsia Species. Frontiers in Microbiology. 1. 139–139. 49 indexed citations
18.
Cardwell, Marissa M. & Juan J. Martinez. (2009). The Sca2 Autotransporter Protein from Rickettsia conorii Is Sufficient To Mediate Adherence to and Invasion of Cultured Mammalian Cells. Infection and Immunity. 77(12). 5272–5280. 72 indexed citations
19.
Chan, Yvonne G. Y., et al.. (2008). Rickettsial outer-membrane protein B (rOmpB) mediates bacterial invasion through Ku70 in an actin, c-Cbl, clathrin and caveolin 2-dependent manner. Cellular Microbiology. 11(4). 629–644. 117 indexed citations
20.
Martinez, Juan J., Stéphanie Seveau, Esteban Veiga, Shigemi Matsuyama, & Pascale Cossart. (2005). Ku70, a Component of DNA-Dependent Protein Kinase, Is a Mammalian Receptor for Rickettsia conorii. Cell. 123(6). 1013–1023. 135 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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