Shih‐Ching Lo

3.9k total citations · 1 hit paper
18 papers, 3.2k citations indexed

About

Shih‐Ching Lo is a scholar working on Molecular Biology, Genetics and Cellular and Molecular Neuroscience. According to data from OpenAlex, Shih‐Ching Lo has authored 18 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 6 papers in Genetics and 3 papers in Cellular and Molecular Neuroscience. Recurrent topics in Shih‐Ching Lo's work include Genomics, phytochemicals, and oxidative stress (7 papers), Glutathione Transferases and Polymorphisms (5 papers) and CRISPR and Genetic Engineering (4 papers). Shih‐Ching Lo is often cited by papers focused on Genomics, phytochemicals, and oxidative stress (7 papers), Glutathione Transferases and Polymorphisms (5 papers) and CRISPR and Genetic Engineering (4 papers). Shih‐Ching Lo collaborates with scholars based in United States, United Kingdom and France. Shih‐Ching Lo's co-authors include Mark Hannink, Donna D. Zhang, Dennis J. Templeton, Janet V. Cross, Morgan Sheng, Michael T. Henzl, Lesa J. Beamer, Daniel J. Whitcomb, Kwangwook Cho and Song Jiao and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Shih‐Ching Lo

17 papers receiving 3.2k citations

Hit Papers

Keap1 Is a Redox-Regulated Substrate Adaptor Protein for ... 2004 2026 2011 2018 2004 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shih‐Ching Lo United States 14 2.5k 416 412 270 236 18 3.2k
Junko Wakabayashi Japan 14 2.5k 1.0× 300 0.7× 484 1.2× 381 1.4× 236 1.0× 18 3.3k
Hiroshi Nomoto Japan 33 1.3k 0.5× 635 1.5× 337 0.8× 185 0.7× 216 0.9× 175 3.3k
Soraya S. Smaili Brazil 33 1.6k 0.6× 503 1.2× 521 1.3× 577 2.1× 302 1.3× 119 3.3k
Oh‐Shin Kwon South Korea 28 1.3k 0.5× 492 1.2× 272 0.7× 235 0.9× 273 1.2× 114 2.4k
Daniel A. Linseman United States 37 2.4k 1.0× 692 1.7× 573 1.4× 253 0.9× 421 1.8× 87 4.3k
Hyun Jin Choi South Korea 30 1.2k 0.5× 499 1.2× 303 0.7× 298 1.1× 139 0.6× 93 2.8k
Sung Bae Lee South Korea 29 1.4k 0.6× 509 1.2× 376 0.9× 463 1.7× 300 1.3× 114 3.1k
Patricia Rada Spain 21 2.1k 0.8× 218 0.5× 414 1.0× 579 2.1× 223 0.9× 47 3.2k
Theodore A. Sarafian United States 23 1.9k 0.8× 604 1.5× 397 1.0× 194 0.7× 166 0.7× 44 3.7k
Hyun Ok Yang South Korea 30 1.1k 0.4× 167 0.4× 318 0.8× 237 0.9× 152 0.6× 95 2.4k

Countries citing papers authored by Shih‐Ching Lo

Since Specialization
Citations

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

Fields of papers citing papers by Shih‐Ching Lo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shih‐Ching Lo. 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 Shih‐Ching Lo. The network helps show where Shih‐Ching Lo may publish in the future.

Co-authorship network of co-authors of Shih‐Ching Lo

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

All Works

18 of 18 papers shown
1.
Nagy, M. Aurel, Stanley Gill, Bin Liu, et al.. (2024). Cis-regulatory elements driving motor neuron-selective viral payload expression within the mammalian spinal cord. Proceedings of the National Academy of Sciences. 121(49). e2418024121–e2418024121.
3.
Peterson, Michael, Helen McLaughlin, Eric Marshall, et al.. (2021). Highly efficient neuronal gene knockout in vivo by CRISPR-Cas9 via neonatal intracerebroventricular injection of AAV in mice. Gene Therapy. 28(10-11). 646–658. 34 indexed citations
4.
Marsh, Galina, Shanqin Xu, Kathryn Koszka, et al.. (2021). Use of CRISPR/Cas9-mediated disruption of CNS cell type genes to profile transduction of AAV by neonatal intracerebroventricular delivery in mice. Gene Therapy. 28(7-8). 456–468. 13 indexed citations
5.
Guilmette, Edward, et al.. (2021). Development of a one-step RT-ddPCR method to determine the expression and potency of AAV vectors. Molecular Therapy — Methods & Clinical Development. 23. 68–77. 7 indexed citations
6.
Lo, Shih‐Ching, Kimberly Scearce‐Levie, & Morgan Sheng. (2016). Characterization of Social Behaviors in caspase-3 deficient mice. Scientific Reports. 6(1). 18335–18335. 41 indexed citations
7.
Lo, Shih‐Ching, Yuan Yuan Wang, Martin Weber, et al.. (2015). Caspase-3 Deficiency Results in Disrupted Synaptic Homeostasis and Impaired Attention Control. Journal of Neuroscience. 35(5). 2118–2132. 31 indexed citations
8.
Hanson, Jesse E., Lunbin Deng, David H. Hackos, et al.. (2013). Histone Deacetylase 2 Cell Autonomously Suppresses Excitatory and Enhances Inhibitory Synaptic Function in CA1 Pyramidal Neurons. Journal of Neuroscience. 33(14). 5924–5929. 29 indexed citations
9.
Whitcomb, Daniel J., Talitha L. Kerrigan, Shih‐Ching Lo, et al.. (2011). Aβ1–42 inhibition of LTP is mediated by a signaling pathway involving caspase-3, Akt1 and GSK-3β. Nature Neuroscience. 14(5). 545–547. 264 indexed citations
10.
Li, Zheng, Jihoon Jo, Jie‐Min Jia, et al.. (2010). Caspase-3 Activation via Mitochondria Is Required for Long-Term Depression and AMPA Receptor Internalization. Cell. 141(5). 859–871. 435 indexed citations
11.
Lo, Shih‐Ching & Mark Hannink. (2008). PGAM5 tethers a ternary complex containing Keap1 and Nrf2 to mitochondria. Experimental Cell Research. 314(8). 1789–1803. 252 indexed citations
12.
Zhou, Wei, et al.. (2007). ERRβ: A potent inhibitor of Nrf2 transcriptional activity. Molecular and Cellular Endocrinology. 278(1-2). 52–62. 44 indexed citations
13.
Lo, Shih‐Ching, et al.. (2006). Structure of the Keap1:Nrf2 interface provides mechanistic insight into Nrf2 signaling. The EMBO Journal. 25(15). 3605–3617. 461 indexed citations
14.
Lo, Shih‐Ching & Mark Hannink. (2006). PGAM5, a Bcl-XL-interacting Protein, Is a Novel Substrate for the Redox-regulated Keap1-dependent Ubiquitin Ligase Complex. Journal of Biological Chemistry. 281(49). 37893–37903. 167 indexed citations
15.
Lo, Shih‐Ching & Mark Hannink. (2006). CAND1-Mediated Substrate Adaptor Recycling Is Required for Efficient Repression of Nrf2 by Keap1. Molecular and Cellular Biology. 26(4). 1235–1244. 84 indexed citations
16.
Zhang, Donna D., Shih‐Ching Lo, Zheng Sun, et al.. (2005). Ubiquitination of Keap1, a BTB-Kelch Substrate Adaptor Protein for Cul3, Targets Keap1 for Degradation by a Proteasome-independent Pathway. Journal of Biological Chemistry. 280(34). 30091–30099. 250 indexed citations
17.
Yu, Sue, Małgorzata Chalimoniuk, Xiaolin Zhang, et al.. (2005). Distinct signaling pathways for induction of type II NOS by IFNγ and LPS in BV-2 microglial cells. Neurochemistry International. 47(4). 298–307. 66 indexed citations
18.
Zhang, Donna D., Shih‐Ching Lo, Janet V. Cross, Dennis J. Templeton, & Mark Hannink. (2004). Keap1 Is a Redox-Regulated Substrate Adaptor Protein for a Cul3-Dependent Ubiquitin Ligase Complex. Molecular and Cellular Biology. 24(24). 10941–10953. 1040 indexed citations breakdown →

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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