Devki Sukhtankar
Impact in
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- Neuropeptides and Animal Physiology
- Neurotransmitter Receptor Influence on Behavior
- Physiology top 10%
- Pain Mechanisms and Treatments
Papers in
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- Neuropeptides and Animal Physiology 10
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- Pain Mechanisms and Treatments 6
- Co-authors
- Mei‐Chuan Ko (12 shared papers)Huiping Ding (7 shared papers)Stephen M. Husbands (4 shared papers)Norikazu Kiguchi (3 shared papers)Paul W. Czoty (3 shared papers)Gerta Cami‐Kobeci (3 shared papers)Nurulain T. Zaveri (1 shared paper)Tamara King (4 shared papers)
- Journals
- The FASEB Journal (3 papers)Blood (3 papers)Pain (2 papers)British Journal of Pharmacology (2 papers)Journal of Pharmacology and Experimental Therapeutics (2 papers)
- Partner nations
- United StatesUnited KingdomJapan
In The Last Decade
Devki Sukhtankar
17 papers receiving 480 citations
Peers
Comparison fields: 5 of 57
- Cellular and Molecular Neuroscience 296
- Physiology 253
- Dermatology 54
- Anesthesiology and Pain Medicine 31
- Sensory Systems 20
Countries citing papers authored by Devki Sukhtankar
This map shows the geographic impact of Devki Sukhtankar'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 Devki Sukhtankar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Devki Sukhtankar more than expected).
Fields of papers citing papers by Devki Sukhtankar
This network shows the impact of papers produced by Devki Sukhtankar. 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 Devki Sukhtankar. The network helps show where Devki Sukhtankar may publish in the future.
Co-authors
The 25 scholars most cited alongside Devki Sukhtankar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 90 | |
| 2 | 2013 | 55 | |
| 3 | 2013 | 46 | |
| 4 | 2015 | 45 | |
| 5 | 2019 | 40 | |
| 6 | 2011 | 31 | |
| 7 | 2015 | 31 | |
| 8 | 2013 | 30 | |
| 9 | 2014 | 29 | |
| 10 | 2015 | 27 | |
| 11 | 2015 | 25 | |
| 12 | 2017 | 22 | |
| 13 | 2017 | 11 | |
| 14 | 2023 | 5 | |
| 15 | 2015 | 2 | |
| 16 | 2022 | 2 | |
| 17 | 2015 | 1 | |
| 18 | 2013 | 0 | |
| 19 | 2025 | 0 | |
| 20 | 2024 | 0 |
About Devki Sukhtankar
Devki Sukhtankar is a scholar working on Cellular and Molecular Neuroscience, Physiology, Molecular Biology, Oncology and Dermatology, having authored 20 papers that have together received 492 indexed citations. Recurring topics across this work include Neuropeptides and Animal Physiology (10 papers), Receptor Mechanisms and Signaling (6 papers), Pain Mechanisms and Treatments (6 papers), Chemokine receptors and signaling (2 papers), Dermatology and Skin Diseases (2 papers), Pain Management and Opioid Use (2 papers), Immune Cell Function and Interaction (2 papers) and Pain Management and Placebo Effect (2 papers). The work is most often cited by research in Cellular and Molecular Neuroscience (296 citations), Physiology (253 citations), Dermatology (54 citations), Anesthesiology and Pain Medicine (31 citations) and Sensory Systems (20 citations). Devki Sukhtankar has collaborated with scholars based in United States, United Kingdom and Japan. Frequent co-authors include Mei‐Chuan Ko, Huiping Ding, Stephen M. Husbands, Norikazu Kiguchi, Paul W. Czoty, Gerta Cami‐Kobeci, Nurulain T. Zaveri, Tamara King, Michael A. Nader and Ken‐ichiro Hayashida. Their work appears in journals such as The FASEB Journal, Blood, Pain, British Journal of Pharmacology and Journal of Pharmacology and Experimental Therapeutics.
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.