Maya E. Kotas

2.5k total citations · 1 hit paper
21 papers, 1.8k citations indexed

About

Maya E. Kotas is a scholar working on Surgery, Physiology and Immunology. According to data from OpenAlex, Maya E. Kotas has authored 21 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Surgery, 7 papers in Physiology and 6 papers in Immunology. Recurrent topics in Maya E. Kotas's work include IL-33, ST2, and ILC Pathways (5 papers), Eosinophilic Esophagitis (4 papers) and Immune Cell Function and Interaction (4 papers). Maya E. Kotas is often cited by papers focused on IL-33, ST2, and ILC Pathways (5 papers), Eosinophilic Esophagitis (4 papers) and Immune Cell Function and Interaction (4 papers). Maya E. Kotas collaborates with scholars based in United States, Switzerland and Tanzania. Maya E. Kotas's co-authors include Ruslan Medzhitov, Richard M. Locksley, Matthew P. Gillum, Gerald I. Shulman, Gerry F. Killeen, Evan Mathenge, Sanjay Bhanot, Jennifer J. Hsiao, Shin Yonemitsu and Derek M. Erion and has published in prestigious journals such as Science, Cell and Proceedings of the National Academy of Sciences.

In The Last Decade

Maya E. Kotas

20 papers receiving 1.8k citations

Hit Papers

Homeostasis, Inflammation, and Disease Susceptibility 2015 2026 2018 2022 2015 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maya E. Kotas United States 16 522 480 426 301 201 21 1.8k
Raja Fayad United States 29 581 1.1× 419 0.9× 373 0.9× 547 1.8× 198 1.0× 47 2.2k
José M. Morán Spain 27 538 1.0× 299 0.6× 326 0.8× 316 1.0× 366 1.8× 103 2.5k
Yasuhiko Nakamura Japan 28 399 0.8× 264 0.6× 483 1.1× 216 0.7× 243 1.2× 97 2.6k
Xiaohong Zhang China 28 730 1.4× 1.2k 2.5× 507 1.2× 305 1.0× 252 1.3× 82 3.2k
Jingjing Fan China 29 954 1.8× 164 0.3× 377 0.9× 278 0.9× 112 0.6× 109 2.2k
Jing Bi China 32 987 1.9× 315 0.7× 259 0.6× 272 0.9× 113 0.6× 102 2.6k
Laura Invidia Italy 7 552 1.1× 394 0.8× 554 1.3× 245 0.8× 88 0.4× 8 1.8k
Aamir Zuberi United States 28 1.2k 2.4× 392 0.8× 892 2.1× 443 1.5× 245 1.2× 61 3.2k
Silvia Manzanero Australia 26 1.0k 2.0× 741 1.5× 397 0.9× 504 1.7× 196 1.0× 64 3.0k
Sarah Gerlo Belgium 27 946 1.8× 679 1.4× 261 0.6× 387 1.3× 173 0.9× 54 2.8k

Countries citing papers authored by Maya E. Kotas

Since Specialization
Citations

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

Fields of papers citing papers by Maya E. Kotas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maya E. Kotas. 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 Maya E. Kotas. The network helps show where Maya E. Kotas may publish in the future.

Co-authorship network of co-authors of Maya E. Kotas

This figure shows the co-authorship network connecting the top 25 collaborators of Maya E. Kotas. A scholar is included among the top collaborators of Maya E. Kotas 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 Maya E. Kotas. Maya E. Kotas 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.
Barron, Jerika J., Sunrae E. Taloma, Madelene W. Dahlgren, et al.. (2024). Group 2 innate lymphoid cells promote inhibitory synapse development and social behavior. Science. 386(6721). eadi1025–eadi1025. 15 indexed citations
2.
Kotas, Maya E., Neil N. Patel, Emily K. Cope, et al.. (2023). IL-13–associated epithelial remodeling correlates with clinical severity in nasal polyposis. Journal of Allergy and Clinical Immunology. 151(5). 1277–1285. 18 indexed citations
3.
Barr, Justinn, Sun‐Young Lee, Maya E. Kotas, et al.. (2022). Injury-induced pulmonary tuft cells are heterogenous, arise independent of key Type 2 cytokines, and are dispensable for dysplastic repair. eLife. 11. 25 indexed citations
4.
Kotas, Maya E., Camille M. Moore, José Gurrola, et al.. (2022). IL-13–programmed airway tuft cells produce PGE2, which promotes CFTR-dependent mucociliary function. JCI Insight. 7(13). 27 indexed citations
5.
Kotas, Maya E., Claire E. O’Leary, & Richard M. Locksley. (2022). Tuft Cells: Context- and Tissue-Specific Programming for a Conserved Cell Lineage. Annual Review of Pathology Mechanisms of Disease. 18(1). 311–335. 28 indexed citations
6.
O’Leary, Claire E., Maya E. Kotas, Johanna Wagner, et al.. (2022). Bile acid–sensitive tuft cells regulate biliary neutrophil influx. Science Immunology. 7(69). eabj1080–eabj1080. 31 indexed citations
7.
Kotas, Maya E., Jérémie Dion, Steven J. Van Dyken, et al.. (2021). A role for IL-33–activated ILC2s in eosinophilic vasculitis. JCI Insight. 6(12). 19 indexed citations
8.
Kotas, Maya E., Satoshi Koga, Hong-Erh Liang, et al.. (2021). CISH constrains the tuft–ILC2 circuit to set epithelial and immune tone. Mucosal Immunology. 14(6). 1295–1305. 14 indexed citations
9.
Kotas, Maya E. & Bruce Thompson. (2021). Toward Optimal Acute Respiratory Distress Syndrome Outcomes. Critical Care Clinics. 37(4). 733–748.
10.
Kotas, Maya E. & Richard M. Locksley. (2018). Why Innate Lymphoid Cells?. Immunity. 48(6). 1081–1090. 82 indexed citations
11.
Kotas, Maya E. & Ruslan Medzhitov. (2015). Homeostasis, Inflammation, and Disease Susceptibility. Cell. 160(5). 816–827. 898 indexed citations breakdown →
12.
Markan, Kathleen R., et al.. (2015). Central Serotonergic Neurons Activate and Recruit Thermogenic Brown and Beige Fat and Regulate Glucose and Lipid Homeostasis. Cell Metabolism. 21(5). 692–705. 68 indexed citations
13.
Erion, Derek M., Maya E. Kotas, Shin Yonemitsu, et al.. (2013). cAMP-responsive Element-binding Protein (CREB)-regulated Transcription Coactivator 2 (CRTC2) Promotes Glucagon Clearance and Hepatic Amino Acid Catabolism to Regulate Glucose Homeostasis. Journal of Biological Chemistry. 288(22). 16167–16176. 18 indexed citations
14.
Kotas, Maya E., et al.. (2013). Sirtuin-1 is a nutrient-dependent modulator of inflammation. Adipocyte. 2(2). 113–118. 38 indexed citations
15.
Kotas, Maya E., Michael J. Jurczak, Matthew P. Gillum, et al.. (2013). Role of caspase-1 in regulation of triglyceride metabolism. Proceedings of the National Academy of Sciences. 110(12). 4810–4815. 65 indexed citations
16.
Kotas, Maya E., Hui‐Young Lee, Matthew P. Gillum, et al.. (2011). Impact of CD1d Deficiency on Metabolism. PLoS ONE. 6(9). e25478–e25478. 69 indexed citations
17.
Gillum, Matthew P., Maya E. Kotas, Derek M. Erion, et al.. (2011). SirT1 Regulates Adipose Tissue Inflammation. Diabetes. 60(12). 3235–3245. 239 indexed citations
18.
Zhang, Qing, James P. Gilligan, W. Mark Saltzman, et al.. (2008). Replacement of Bone Marrow by Bone in Rat Femurs: The Bone Bioreactor. Tissue Engineering Part A. 14(2). 237–246. 15 indexed citations
19.
Zhang, Qing, James Gilligan, Hua-Zhu Ke, et al.. (2008). Replacement of Bone Marrow by Bone in Rat Femurs: The Bone Bioreactor. Tissue Engineering. 2883342133–2883342133. 3 indexed citations
20.
Killeen, Gerry F., Japhet Kihonda, E. Lyimo, et al.. (2006). Quantifying behavioural interactions between humans and mosquitoes: Evaluating the protective efficacy of insecticidal nets against malaria transmission in rural Tanzania. BMC Infectious Diseases. 6(1). 161–161. 123 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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