May Khanna

3.1k total citations
56 papers, 2.0k citations indexed

About

May Khanna is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Physiology. According to data from OpenAlex, May Khanna has authored 56 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 18 papers in Cellular and Molecular Neuroscience and 16 papers in Physiology. Recurrent topics in May Khanna's work include Pain Mechanisms and Treatments (12 papers), Axon Guidance and Neuronal Signaling (11 papers) and RNA Research and Splicing (9 papers). May Khanna is often cited by papers focused on Pain Mechanisms and Treatments (12 papers), Axon Guidance and Neuronal Signaling (11 papers) and RNA Research and Splicing (9 papers). May Khanna collaborates with scholars based in United States, China and South Korea. May Khanna's co-authors include Rajesh Khanna, Liberty François‐Moutal, Aubin Moutal, Samantha Perez‐Miller, David D. Scott, Samy O. Meroueh, Xiaofang Yang, Erik T. Dustrude, Yuying Wang and Klaus Schlichte and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

May Khanna

54 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
May Khanna United States 30 1.2k 534 477 231 191 56 2.0k
Luciana Romão Brazil 26 817 0.7× 484 0.9× 275 0.6× 269 1.2× 206 1.1× 55 2.0k
Hong‐Yu Hu China 26 1.3k 1.1× 278 0.5× 354 0.7× 470 2.0× 298 1.6× 83 1.9k
Xiaojiang Li China 21 1.0k 0.9× 843 1.6× 188 0.4× 287 1.2× 242 1.3× 82 1.9k
Lakshmanan K. Iyer United States 24 1.1k 0.9× 280 0.5× 235 0.5× 268 1.2× 138 0.7× 50 2.4k
Graziella Cappelletti Italy 27 949 0.8× 500 0.9× 330 0.7× 475 2.1× 343 1.8× 81 2.0k
Xianbo Zhou United States 18 2.0k 1.7× 457 0.9× 266 0.6× 155 0.7× 77 0.4× 38 2.6k
Jagdeep K. Sandhu Canada 26 1.4k 1.2× 299 0.6× 253 0.5× 204 0.9× 115 0.6× 57 2.7k
Subramaniam Ganesh India 31 1.1k 1.0× 290 0.5× 705 1.5× 430 1.9× 170 0.9× 113 2.8k
Jung Jin Hwang South Korea 27 1.4k 1.2× 231 0.4× 276 0.6× 92 0.4× 209 1.1× 75 2.7k
Qianwa Liang United States 22 973 0.8× 161 0.3× 483 1.0× 178 0.8× 103 0.5× 38 2.5k

Countries citing papers authored by May Khanna

Since Specialization
Citations

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

Fields of papers citing papers by May Khanna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by May Khanna. 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 May Khanna. The network helps show where May Khanna may publish in the future.

Co-authorship network of co-authors of May Khanna

This figure shows the co-authorship network connecting the top 25 collaborators of May Khanna. A scholar is included among the top collaborators of May Khanna 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 May Khanna. May Khanna 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.
Ambrose, Andrew J., Jared Sivinski, Xiaoyi Zhu, et al.. (2024). Human Hsp70 Substrate-Binding Domains Recognize Distinct Client Proteins. Biochemistry. 63(3). 251–263. 3 indexed citations
2.
Gómez, Kimberly, Harrison J. Stratton, Cheng Tang, et al.. (2023). Identification and targeting of a unique Na V 1.7 domain driving chronic pain. Proceedings of the National Academy of Sciences. 120(32). e2217800120–e2217800120. 23 indexed citations
3.
François‐Moutal, Liberty, David D. Scott, Andrew J. Ambrose, et al.. (2022). Heat shock protein Grp78/BiP/HspA5 binds directly to TDP-43 and mitigates toxicity associated with disease pathology. Scientific Reports. 12(1). 8140–8140. 25 indexed citations
4.
François‐Moutal, Liberty, et al.. (2022). Aptamers Targeting Hallmark Proteins of Neurodegeneration. Nucleic Acid Therapeutics. 32(4). 235–250. 6 indexed citations
5.
6.
Khanna, Rajesh, Jie Yu, Xiaofang Yang, et al.. (2019). Targeting the CaVα–CaVβ interaction yields an antagonist of the N-type CaV2.2 channel with broad antinociceptive efficacy. Pain. 160(7). 1644–1661. 35 indexed citations
7.
Moutal, Aubin, et al.. (2019). Evaluation of edonerpic maleate as a CRMP2 inhibitor for pain relief. Channels. 13(1). 498–504. 2 indexed citations
8.
Zhou, Yuan, Song Cai, Aubin Moutal, et al.. (2019). The Natural Flavonoid Naringenin Elicits Analgesia through Inhibition of NaV1.8 Voltage-Gated Sodium Channels. ACS Chemical Neuroscience. 10(12). 4834–4846. 28 indexed citations
9.
François‐Moutal, Liberty, David D. Scott, Samantha Perez‐Miller, et al.. (2018). Chemical shift perturbation mapping of the Ubc9-CRMP2 interface identifies a pocket in CRMP2 amenable for allosteric modulation of Nav1.7 channels. Channels. 12(1). 219–227. 16 indexed citations
10.
François‐Moutal, Liberty, Erik T. Dustrude, Yue Wang, et al.. (2018). Inhibition of the Ubc9 E2 SUMO-conjugating enzyme-CRMP2 interaction decreases NaV1.7 currents and reverses experimental neuropathic pain. PMC. 1 indexed citations
11.
Moutal, Aubin, Xiaofang Yang, Wennan Li, et al.. (2017). CRISPR/Cas9 editing of Nf1 gene identifies CRMP2 as a therapeutic target in neurofibromatosis type 1-related pain that is reversed by (S)-Lacosamide. Pain. 158(12). 2301–2319. 63 indexed citations
12.
Dustrude, Erik T., Samantha Perez‐Miller, Liberty François‐Moutal, et al.. (2017). A single structurally conserved SUMOylation site in CRMP2 controls NaV1.7 function. Channels. 11(4). 316–328. 33 indexed citations
13.
Yan, Chao, Degang Liu, Liwei Li, et al.. (2014). Discovery and characterization of small molecules that target the GTPase Ral. Nature. 515(7527). 443–447. 112 indexed citations
14.
Khanna, May, Tsuyoshi Imasaki, Vimbai M. Chikwana, et al.. (2013). Expression and purification of functional human glycogen synthase-1 (hGYS1) in insect cells. Protein Expression and Purification. 90(2). 78–83. 10 indexed citations
15.
Ripsch, Matthew S., Carrie Ballard, May Khanna, et al.. (2012). A peptide uncoupling CRMP-2 from the presynaptic Ca2+ channel complex demonstrates efficacy in animal models of migraine and AIDS therapy-induced neuropathy. Translational Neuroscience. 3(1). 1–8. 32 indexed citations
16.
Wang, Fang, William E. Knabe, Liwei Li, et al.. (2012). Design, synthesis, biochemical studies, cellular characterization, and structure-based computational studies of small molecules targeting the urokinase receptor. Bioorganic & Medicinal Chemistry. 20(15). 4760–4773. 36 indexed citations
17.
Khanna, May, Ann C. Kimble-Hill, Bibek Parajuli, et al.. (2011). Discovery of a Novel Class of Covalent Inhibitor for Aldehyde Dehydrogenases. Journal of Biological Chemistry. 286(50). 43486–43494. 65 indexed citations
18.
Khanna, May, Harm van Bakel, Xinyi Tang, et al.. (2009). A systematic characterization of Cwc21, the yeast ortholog of the human spliceosomal protein SRm300. RNA. 15(12). 2174–2185. 28 indexed citations
19.
Khanna, May, Huey‐Nan Wu, Carina Johansson, Michèle Caizergues‐Ferrer, & Juli Feigon. (2005). Structural study of the H/ACA snoRNP components Nop10p and the 3′ hairpin of U65 snoRNA. RNA. 12(1). 40–52. 33 indexed citations
20.
Khanna, May, et al.. (1976). Effect of certain chemical agents on sterol formation by Saccharomyces cerevisiae.. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich). 14(6). 729–30. 3 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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