Sang Su Oh

1.2k total citations · 1 hit paper
17 papers, 949 citations indexed

About

Sang Su Oh is a scholar working on Physiology, Neurology and Genetics. According to data from OpenAlex, Sang Su Oh has authored 17 papers receiving a total of 949 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Physiology, 5 papers in Neurology and 4 papers in Genetics. Recurrent topics in Sang Su Oh's work include Pain Mechanisms and Treatments (5 papers), Amyotrophic Lateral Sclerosis Research (4 papers) and Neurogenetic and Muscular Disorders Research (3 papers). Sang Su Oh is often cited by papers focused on Pain Mechanisms and Treatments (5 papers), Amyotrophic Lateral Sclerosis Research (4 papers) and Neurogenetic and Muscular Disorders Research (3 papers). Sang Su Oh collaborates with scholars based in United States, Canada and Japan. Sang Su Oh's co-authors include Eva L. Feldman, John M. Hayes, Carey Backus, Kelli A. Sullivan, Yu Hong, Frank C. Brosius, Timothy D. Wiggin, Stephen I. Lentz, Hsinlin T. Cheng and Jacqueline R. Dauch and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Cancer Research and Diabetes.

In The Last Decade

Sang Su Oh

17 papers receiving 934 citations

Hit Papers

Mouse and Rat Anesthesia and Analgesia 2024 2026 2025 2024 10 20 30 40
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Mouse and Rat Anesthesia and Analgesia
    2024 40 citations Sang Su Oh et al. Current Protocols profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sang Su Oh United States 14 371 342 235 201 149 17 949
Yasuhiro Watanabe Japan 21 422 1.1× 294 0.9× 358 1.5× 188 0.9× 219 1.5× 60 1.2k
Nádia Pereira Gonçalves Portugal 15 332 0.9× 301 0.9× 207 0.9× 262 1.3× 43 0.3× 26 897
Judyta K. Juranek Poland 17 321 0.9× 306 0.9× 269 1.1× 128 0.6× 61 0.4× 43 1.1k
Carla Porretta‐Serapiglia Italy 16 205 0.6× 265 0.8× 197 0.8× 144 0.7× 48 0.3× 17 651
Arnaud Bocquet France 14 316 0.9× 116 0.3× 205 0.9× 147 0.7× 70 0.5× 24 900
Marie‐Françoise Ritz Switzerland 19 349 0.9× 120 0.4× 145 0.6× 214 1.1× 68 0.5× 41 893
Jing Xiong China 17 419 1.1× 216 0.6× 221 0.9× 168 0.8× 37 0.2× 45 1.1k
Silvia Belia Italy 18 757 2.0× 309 0.9× 347 1.5× 207 1.0× 244 1.6× 42 1.4k
Parichehr Pasbakhsh Iran 21 372 1.0× 213 0.6× 111 0.5× 226 1.1× 184 1.2× 78 1.3k
Honglin Feng China 18 415 1.1× 153 0.4× 340 1.4× 125 0.6× 117 0.8× 43 991

Countries citing papers authored by Sang Su Oh

Since Specialization
Citations

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

Fields of papers citing papers by Sang Su Oh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sang Su Oh. 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 Sang Su Oh. The network helps show where Sang Su Oh may publish in the future.

Co-authorship network of co-authors of Sang Su Oh

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

All Works

17 of 17 papers shown
1.
Oh, Sang Su, et al.. (2024). Mouse and Rat Anesthesia and Analgesia. Current Protocols. 4(2). e995–e995. 40 indexed citations breakdown →
2.
Zhang, Linlin, Hong-Tao Wang, Heung Yong Jin, Je Hyeong Kim, & Sang Su Oh. (2020). Common Salivary Protein 1 in Saliva of Diabetes Patients (II). Clinical Laboratory. 66(12/2020). 3 indexed citations
3.
Rosko, Andrew J., Robbi A. Kupfer, Sang Su Oh, et al.. (2017). Immunohistologic analysis of spontaneous recurrent laryngeal nerve reinnervation in a rat model. The Laryngoscope. 128(3). E117–E122. 13 indexed citations
4.
Kashlan, Osama N., Sang Su Oh, Lisa M. McGinley, et al.. (2015). Histological Bulbar Manifestations in the ALS Rat. Neurodegenerative Diseases. 15(2). 121–126. 7 indexed citations
5.
Nguyen, Tammy, Sang Su Oh, David T. Weaver, et al.. (2014). Loss of Miro1-directed mitochondrial movement results in a novel murine model for neuron disease. Proceedings of the National Academy of Sciences. 111(35). E3631–40. 171 indexed citations
6.
Kupfer, Robbi A., Matthew Old, Sang Su Oh, Eva L. Feldman, & Norman D. Hogikyan. (2013). Spontaneous laryngeal reinnervation following chronic recurrent laryngeal nerve injury. The Laryngoscope. 123(9). 2216–2227. 20 indexed citations
7.
Dauch, Jacqueline R., et al.. (2012). Neuron–astrocyte signaling network in spinal cord dorsal horn mediates painful neuropathy of type 2 diabetes. Glia. 60(9). 1301–1315. 35 indexed citations
8.
Sakowski, Stacey A., J. Simon Lunn, Sang Su Oh, et al.. (2012). Neuromuscular effects of G93A-SOD1 expression in zebrafish. Molecular Neurodegeneration. 7(1). 44–44. 56 indexed citations
9.
Pande, Manjusha, Junguk Hur, Yu Hong, et al.. (2011). Transcriptional Profiling of Diabetic Neuropathy in the BKSdb/dbMouse. Diabetes. 60(7). 1981–1989. 89 indexed citations
10.
Old, Matthew, Sang Su Oh, Eva L. Feldman, & Norman D. Hogikyan. (2011). Novel Model to Assess Laryngeal Function, Innervation, and Reinnervation. Annals of Otology Rhinology & Laryngology. 120(5). 331–338. 7 indexed citations
11.
Kakoki, Masao, Kelli A. Sullivan, Carey Backus, et al.. (2010). Lack of both bradykinin B1 and B2 receptors enhances nephropathy, neuropathy, and bone mineral loss in Akita diabetic mice. Proceedings of the National Academy of Sciences. 107(22). 10190–10195. 99 indexed citations
12.
Oh, Sang Su, John M. Hayes, Catrina Sims‐Robinson, Kelli A. Sullivan, & Eva L. Feldman. (2010). The effects of anesthesia on measures of nerve conduction velocity in male C57Bl6/J mice. Neuroscience Letters. 483(2). 127–131. 57 indexed citations
13.
Cheng, Hsinlin T., Jacqueline R. Dauch, Sang Su Oh, et al.. (2010). p38 Mediates Mechanical Allodynia in a Mouse Model of Type 2 Diabetes. Molecular Pain. 6. 28–28. 54 indexed citations
14.
Sakowski, Stacey A., J. Simon Lunn, Kenneth Fung, et al.. (2009). Neuroprotection using gene therapy to induce vascular endothelial growth factor-A expression. Gene Therapy. 16(11). 1292–1299. 29 indexed citations
15.
Franz, Colin K., Thais Federici, Jun Yang, et al.. (2008). Intraspinal cord delivery of IGF-I mediated by adeno-associated virus 2 is neuroprotective in a rat model of familial ALS. Neurobiology of Disease. 33(3). 473–481. 63 indexed citations
16.
Sullivan, Kelli A., John M. Hayes, Timothy D. Wiggin, et al.. (2007). Mouse models of diabetic neuropathy. Neurobiology of Disease. 28(3). 276–285. 151 indexed citations
17.
Golen, Cynthia M. van, Bhumsoo Kim, Mary Soules, et al.. (2006). Insulin-Like Growth Factor-I Receptor Expression Regulates Neuroblastoma Metastasis to Bone. Cancer Research. 66(13). 6570–6578. 55 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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