John P. Pribis

1.7k total citations
19 papers, 914 citations indexed

About

John P. Pribis is a scholar working on Genetics, Molecular Biology and Immunology. According to data from OpenAlex, John P. Pribis has authored 19 papers receiving a total of 914 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Genetics, 6 papers in Molecular Biology and 6 papers in Immunology. Recurrent topics in John P. Pribis's work include Immune Response and Inflammation (5 papers), Bacterial Genetics and Biotechnology (4 papers) and Advanced Glycation End Products research (4 papers). John P. Pribis is often cited by papers focused on Immune Response and Inflammation (5 papers), Bacterial Genetics and Biotechnology (4 papers) and Advanced Glycation End Products research (4 papers). John P. Pribis collaborates with scholars based in United States, China and Israel. John P. Pribis's co-authors include Timothy R. Billiar, Juan B. Ochoa, Susan M. Rosenberg, Melanie J. Scott, P. J. Hastings, Yin Zhai, Xinmei Zhu, Yoram Vodovotz, Sodam Kim and Patricia Loughran and has published in prestigious journals such as Molecular Cell, PLoS ONE and Annals of Surgery.

In The Last Decade

John P. Pribis

19 papers receiving 895 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John P. Pribis United States 16 331 227 134 125 103 19 914
S Gollapudi United States 21 442 1.3× 414 1.8× 68 0.5× 44 0.4× 93 0.9× 47 1.4k
Manuel Conde Spain 18 430 1.3× 202 0.9× 61 0.5× 38 0.3× 86 0.8× 49 1.3k
Dustin Singer Switzerland 13 664 2.0× 57 0.3× 214 1.6× 107 0.9× 109 1.1× 14 1.3k
Jianling Li China 14 278 0.8× 135 0.6× 39 0.3× 25 0.2× 61 0.6× 64 945
Janka Bábíčková Slovakia 19 445 1.3× 160 0.7× 125 0.9× 29 0.2× 126 1.2× 46 1.3k
John E. Burkhardt United States 15 243 0.7× 143 0.6× 76 0.6× 23 0.2× 134 1.3× 38 1.1k
Xiaojiao Zhang China 22 603 1.8× 82 0.4× 51 0.4× 20 0.2× 83 0.8× 81 1.2k
Xiaolei Hu China 20 299 0.9× 69 0.3× 98 0.7× 20 0.2× 78 0.8× 48 929

Countries citing papers authored by John P. Pribis

Since Specialization
Citations

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

Fields of papers citing papers by John P. Pribis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John P. Pribis. 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 John P. Pribis. The network helps show where John P. Pribis may publish in the future.

Co-authorship network of co-authors of John P. Pribis

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

All Works

19 of 19 papers shown
1.
Zhai, Yin, John P. Pribis, Libertad Garcı́a-Villada, et al.. (2023). ppGpp and RNA-polymerase backtracking guide antibiotic-induced mutable gambler cells. Molecular Cell. 83(8). 1298–1310.e4. 14 indexed citations
2.
Zhai, Yin, John P. Pribis, Sean W. Dooling, et al.. (2023). Drugging evolution of antibiotic resistance at a regulatory network hub. Science Advances. 9(25). eadg0188–eadg0188. 17 indexed citations
3.
Pribis, John P., Yin Zhai, P. J. Hastings, & Susan M. Rosenberg. (2022). Stress-Induced Mutagenesis, Gambler Cells, and Stealth Targeting Antibiotic-Induced Evolution. mBio. 13(3). e0107422–e0107422. 34 indexed citations
4.
Mei, Qian, Devon M. Fitzgerald, Jingjing Liu, et al.. (2021). Two mechanisms of chromosome fragility at replication-termination sites in bacteria. Science Advances. 7(25). 14 indexed citations
5.
Pribis, John P., Libertad Garcı́a-Villada, Yin Zhai, et al.. (2019). Gamblers: An Antibiotic-Induced Evolvable Cell Subpopulation Differentiated by Reactive-Oxygen-Induced General Stress Response. Molecular Cell. 74(4). 785–800.e7. 124 indexed citations
6.
Han, SeungHye, Tolani F. Olonisakin, John P. Pribis, et al.. (2017). A checklist is associated with increased quality of reporting preclinical biomedical research: A systematic review. PLoS ONE. 12(9). e0183591–e0183591. 80 indexed citations
7.
Xia, Jun, Qian Mei, Chien-Hui Ma, et al.. (2016). Holliday junction trap shows how cells use recombination and a junction-guardian role of RecQ helicase. Science Advances. 2(11). e1601605–e1601605. 29 indexed citations
8.
Pribis, John P., Yousef Al‐Abed, Huan Yang, et al.. (2015). The HIV Protease Inhibitor Saquinavir Inhibits HMGBl-Driven Inflammation by Targeting the Interaction of Cathepsin V with TLR4/MyD88. Molecular Medicine. 21(1). 749–757. 18 indexed citations
9.
Cai, Jingjing, Hong Yuan, Qingde Wang, et al.. (2015). HMGB1-Driven Inflammation and Intimal Hyperplasia After Arterial Injury Involves Cell-Specific Actions Mediated by TLR4. Arteriosclerosis Thrombosis and Vascular Biology. 35(12). 2579–2593. 62 indexed citations
10.
Lü, Jun, et al.. (2015). Epsilon aminocaproic acid reduces blood transfusion and improves the coagulation test after pediatric open-heart surgery: a meta-analysis of 5 clinical trials.. PubMed. 8(7). 7978–87. 17 indexed citations
11.
Zhu, Xinmei, John P. Pribis, Paulo C. Rodrı́guez, et al.. (2013). The Central Role of Arginine Catabolism in T-Cell Dysfunction and Increased Susceptibility to Infection After Physical Injury. Annals of Surgery. 259(1). 171–178. 90 indexed citations
12.
Gerő, Domokos, Petra Szoleczky, Katalin Módis, et al.. (2013). Identification of Pharmacological Modulators of HMGB1-Induced Inflammatory Response by Cell-Based Screening. PLoS ONE. 8(6). e65994–e65994. 31 indexed citations
13.
Kim, Sodam, Sun Young Kim, John P. Pribis, et al.. (2013). Signaling of High Mobility Group Box 1 (HMGB1) through Toll-like Receptor 4 in Macrophages Requires CD14. Molecular Medicine. 19(1). 88–98. 138 indexed citations
14.
Seymour, Christopher, Sachin Yende, Melanie J. Scott, et al.. (2013). Metabolomics in pneumonia and sepsis: an analysis of the GenIMS cohort study. Intensive Care Medicine. 39(8). 1423–1434. 85 indexed citations
15.
Sun, Qiang, Tomohiro Kawamura, Kosuke Masutani, et al.. (2012). Oral intake of hydrogen-rich water inhibits intimal hyperplasia in arterialized vein grafts in rats. Cardiovascular Research. 94(1). 144–153. 43 indexed citations
16.
Kim, Sun Young, et al.. (2011). New clinical score for disease activity at diagnosis in Langerhans cell histiocytosis. The Korean Journal of Hematology. 46(3). 186–186. 2 indexed citations
17.
Pribis, John P., Xinmei Zhu, Yoram Vodovotz, & Juan B. Ochoa. (2011). Systemic Arginine Depletion After a Murine Model of Surgery or Trauma. Journal of Parenteral and Enteral Nutrition. 36(1). 53–59. 28 indexed citations
18.
Popovič, P., et al.. (2010). Nature of Myeloid Cells Expressing Arginase 1 in Peripheral Blood After Trauma. The Journal of Trauma: Injury, Infection, and Critical Care. 68(4). 843–852. 39 indexed citations
19.
Popovič, P., et al.. (2009). Stat 6-Dependent Induction of Myeloid Derived Suppressor Cells After Physical Injury Regulates Nitric Oxide Response to Endotoxin. Annals of Surgery. 251(1). 120–126. 49 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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