Imir G. Metushi

1.4k total citations
29 papers, 1.0k citations indexed

About

Imir G. Metushi is a scholar working on Pharmacology, Oncology and Pharmacology. According to data from OpenAlex, Imir G. Metushi has authored 29 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Pharmacology, 11 papers in Oncology and 7 papers in Pharmacology. Recurrent topics in Imir G. Metushi's work include Drug-Induced Hepatotoxicity and Protection (14 papers), Drug Transport and Resistance Mechanisms (10 papers) and Pharmacogenetics and Drug Metabolism (8 papers). Imir G. Metushi is often cited by papers focused on Drug-Induced Hepatotoxicity and Protection (14 papers), Drug Transport and Resistance Mechanisms (10 papers) and Pharmacogenetics and Drug Metabolism (8 papers). Imir G. Metushi collaborates with scholars based in United States, Canada and Denmark. Imir G. Metushi's co-authors include Jack Uetrecht, Elizabeth Phillips, M. Anthony Hayes, Tetsuya Nakagawa, William M. Lee, Bjoern Peters, Corron Sanders, Robert L. Fitzgerald, Søren Buus and Elizabeth J. Phillips and has published in prestigious journals such as Bioinformatics, PLoS ONE and Hepatology.

In The Last Decade

Imir G. Metushi

28 papers receiving 977 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Imir G. Metushi United States 15 495 240 232 181 171 29 1.0k
B K Park United Kingdom 14 423 0.9× 186 0.8× 299 1.3× 59 0.3× 133 0.8× 26 1.1k
Nicolas Hohmann Germany 16 254 0.5× 272 1.1× 185 0.8× 118 0.7× 123 0.7× 36 1.0k
N.R. Kitteringham United Kingdom 20 345 0.7× 250 1.0× 281 1.2× 98 0.5× 158 0.9× 33 971
B.K. Park United Kingdom 20 557 1.1× 213 0.9× 239 1.0× 112 0.6× 224 1.3× 57 1.3k
Ken Grime United Kingdom 18 557 1.1× 404 1.7× 271 1.2× 63 0.3× 70 0.4× 36 1.1k
Marina Kacevska Australia 14 373 0.8× 384 1.6× 364 1.6× 66 0.4× 97 0.6× 14 1.2k
Camilla Stephens Spain 19 1.0k 2.1× 324 1.4× 173 0.7× 38 0.2× 109 0.6× 44 1.5k
Sumathy Mathialagan United States 21 300 0.6× 643 2.7× 341 1.5× 274 1.5× 143 0.8× 41 1.3k
S P Spielberg Canada 15 415 0.8× 183 0.8× 192 0.8× 223 1.2× 927 5.4× 22 1.8k
Abdelhakim Ahmed‐Belkacem France 17 117 0.2× 379 1.6× 502 2.2× 59 0.3× 149 0.9× 31 1.2k

Countries citing papers authored by Imir G. Metushi

Since Specialization
Citations

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

Fields of papers citing papers by Imir G. Metushi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Imir G. Metushi. 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 Imir G. Metushi. The network helps show where Imir G. Metushi may publish in the future.

Co-authorship network of co-authors of Imir G. Metushi

This figure shows the co-authorship network connecting the top 25 collaborators of Imir G. Metushi. A scholar is included among the top collaborators of Imir G. Metushi 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 Imir G. Metushi. Imir G. Metushi 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.
Metushi, Imir G., et al.. (2023). A case of false positive opiate immunoassay results from rifampin (rifampicin) treatment. Practical Laboratory Medicine. 37. e00334–e00334. 1 indexed citations
2.
3.
Metushi, Imir G., Aaron Schneir, & Robert L. Fitzgerald. (2017). Identification of Novel Synthetic Opioid U-47700 through a Broad Screen Time of Flight High-Resolution Mass Spectrometry Method. American Journal of Clinical Pathology. 147(suppl_2). S165–S165. 5 indexed citations
4.
Metushi, Imir G., Jack Uetrecht, & Elizabeth Phillips. (2016). Mechanism of isoniazid‐induced hepatotoxicity: then and now. British Journal of Clinical Pharmacology. 81(6). 1030–1036. 155 indexed citations
5.
Schricker, Amir, et al.. (2016). High-dose loperamide abuse–associated ventricular arrhythmias. HeartRhythm Case Reports. 2(3). 232–236. 20 indexed citations
6.
Liu, Feng, Ping Cai, Imir G. Metushi, et al.. (2016). Exploring an animal model of amodiaquine-induced liver injury in rats and mice. Journal of Immunotoxicology. 13(5). 694–712. 6 indexed citations
7.
Metushi, Imir G., Robert L. Fitzgerald, & Iain M. McIntyre. (2016). Assessment and Comparison of Vitreous Humor as an Alternative Matrix for Forensic Toxicology Screening by GC–MS. Journal of Analytical Toxicology. 40(4). 243–247. 26 indexed citations
8.
Saitman, Alec, et al.. (2015). Evaluation of the Waters MassTrak LC–MS/MS Assay for Tacrolimus and a Comparison to the Abbott Architect Immunoassay. Therapeutic Drug Monitoring. 38(3). 300–304. 15 indexed citations
9.
Metushi, Imir G., Amanda Wriston, Priyanka Banerjee, et al.. (2015). Acyclovir Has Low but Detectable Influence on HLA-B*57:01 Specificity without Inducing Hypersensitivity. PLoS ONE. 10(5). e0124878–e0124878. 10 indexed citations
10.
Trolle, Thomas, Imir G. Metushi, Jason Greenbaum, et al.. (2015). Automated benchmarking of peptide-MHC class I binding predictions. Bioinformatics. 31(13). 2174–2181. 103 indexed citations
11.
Pavlos, Rebecca, S. Mallal, David A. Ostrov, et al.. (2014). T Cell–Mediated Hypersensitivity Reactions to Drugs. Annual Review of Medicine. 66(1). 439–454. 105 indexed citations
12.
Metushi, Imir G., Ping Cai, Dzana Dervovic, et al.. (2014). Development of a novel mouse model of amodiaquine-induced liver injury with a delayed onset. Journal of Immunotoxicology. 12(3). 247–260. 34 indexed citations
13.
Metushi, Imir G., Xu Zhu, & Jack Uetrecht. (2014). D-penicillamine-induced granulomatous hepatitis in brown Norway rats. Molecular and Cellular Biochemistry. 393(1-2). 229–235. 4 indexed citations
14.
Metushi, Imir G., Ping Cai, Libia Vega, Denis M. Grant, & Jack Uetrecht. (2014). Paradoxical Attenuation of Autoimmune Hepatitis by Oral Isoniazid in Wild-Type and N-Acetyltransferase–Deficient Mice. Drug Metabolism and Disposition. 42(6). 963–973. 10 indexed citations
15.
Metushi, Imir G., M. Anthony Hayes, & Jack Uetrecht. (2014). Treatment of PD‐1−/− mice with amodiaquine and anti‐CTLA4 leads to liver injury similar to idiosyncratic liver injury in patients. Hepatology. 61(4). 1332–1342. 112 indexed citations
16.
Metushi, Imir G., et al.. (2013). Hepatic effects of aminoglutethimide: A model aromatic amine. Journal of Immunotoxicology. 12(1). 24–32. 3 indexed citations
17.
Metushi, Imir G. & Jack Uetrecht. (2013). Lack of liver injury in Wistar rats treated with the combination of isoniazid and rifampicin. Molecular and Cellular Biochemistry. 387(1-2). 9–17. 15 indexed citations
18.
Metushi, Imir G. & Jack Uetrecht. (2013). Isoniazid-induced liver injury and immune response in mice. Journal of Immunotoxicology. 11(4). 383–392. 30 indexed citations
19.
Metushi, Imir G., Corron Sanders, William M. Lee, & Jack Uetrecht. (2013). Detection of anti-isoniazid and anti–cytochrome P450 antibodies in patients with isoniazid-induced liver failure. Hepatology. 59(3). 1084–1093. 102 indexed citations
20.
Metushi, Imir G., et al.. (2012). Titanium(IV) Complexes of Disulfide-Linked Schiff Bases. Inorganic Chemistry. 51(9). 5138–5145. 12 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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