Min Pi

3.8k total citations · 1 hit paper
55 papers, 3.1k citations indexed

About

Min Pi is a scholar working on Molecular Biology, Nephrology and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Min Pi has authored 55 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Molecular Biology, 14 papers in Nephrology and 13 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Min Pi's work include Parathyroid Disorders and Treatments (14 papers), Metabolism, Diabetes, and Cancer (8 papers) and Diabetes Treatment and Management (8 papers). Min Pi is often cited by papers focused on Parathyroid Disorders and Treatments (14 papers), Metabolism, Diabetes, and Cancer (8 papers) and Diabetes Treatment and Management (8 papers). Min Pi collaborates with scholars based in United States, China and Japan. Min Pi's co-authors include L. Darryl Quarles, Shiguang Liu, Qisheng Tu, Yunpeng Wu, Qiang Luo, Wen Tang, Jianping Zhou, Jason R. Stubbs, Rui-Song Ye and Robert F. Spurney and has published in prestigious journals such as Journal of Biological Chemistry, Journal of Clinical Investigation and PLoS ONE.

In The Last Decade

Min Pi

53 papers receiving 3.0k citations

Hit Papers

Fibroblast Growth Factor 23 Is a Counter-Regulatory Phosp... 2006 2026 2012 2019 2006 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Min Pi United States 28 1.6k 994 624 568 501 55 3.1k
Clemens Bergwitz United States 26 1.3k 0.8× 1.4k 1.4× 198 0.3× 905 1.6× 517 1.0× 58 3.3k
Robert R. Butters United States 24 2.0k 1.3× 1.8k 1.8× 241 0.4× 433 0.8× 562 1.1× 27 4.0k
Edward F. Nemeth United States 40 2.5k 1.6× 2.6k 2.6× 233 0.4× 646 1.1× 709 1.4× 87 5.3k
Tadashi Kaname Japan 20 1.8k 1.1× 2.0k 2.1× 172 0.3× 1.5k 2.6× 309 0.6× 40 4.4k
Adam Sun United States 14 1.6k 1.0× 1.1k 1.1× 127 0.2× 314 0.6× 296 0.6× 21 3.0k
Toshiaki Monkawa Japan 31 1.7k 1.1× 521 0.5× 286 0.5× 217 0.4× 148 0.3× 68 2.9k
Hiroshi Kurosu Japan 24 3.5k 2.2× 3.4k 3.4× 322 0.5× 2.4k 4.3× 381 0.8× 41 7.2k
Michael R. Bösl Germany 28 3.4k 2.1× 133 0.1× 273 0.4× 498 0.9× 429 0.9× 40 5.0k
Morichika Konishi Japan 38 3.6k 2.3× 285 0.3× 259 0.4× 692 1.2× 263 0.5× 87 5.1k
Masashi Isshiki Japan 17 2.7k 1.7× 307 0.3× 160 0.3× 169 0.3× 1.2k 2.4× 36 3.8k

Countries citing papers authored by Min Pi

Since Specialization
Citations

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

Fields of papers citing papers by Min Pi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Min Pi

This figure shows the co-authorship network connecting the top 25 collaborators of Min Pi. A scholar is included among the top collaborators of Min Pi 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 Min Pi. Min Pi 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.
Xing, Ying, Min Pi, Runshun Zhang, & Tiancai Wen. (2021). Study on the TCM Syndromes Evolution and Chinese Herbal Characteristics of Type 2 Diabetes Patients with Different Courses of Disease in TCM “Heat Stage”: A Real-World Study. Evidence-based Complementary and Alternative Medicine. 2021. 1–12. 4 indexed citations
2.
Pi, Min, Fuyi Xu, Rui-Song Ye, et al.. (2020). Humanized GPRC6AKGKY is a gain-of-function polymorphism in mice. Scientific Reports. 10(1). 11143–11143. 12 indexed citations
3.
Kapoor, Karan, et al.. (2020). The carboxylation status of osteocalcin has important consequences for its structure and dynamics. Biochimica et Biophysica Acta (BBA) - General Subjects. 1865(3). 129809–129809. 12 indexed citations
4.
Ye, Rui-Song, Min Pi, John V. Cox, Satoru K. Nishimoto, & L. Darryl Quarles. (2017). CRISPR/Cas9 targeting of GPRC6A suppresses prostate cancer tumorigenesis in a human xenograft model. Journal of Experimental & Clinical Cancer Research. 36(1). 90–90. 58 indexed citations
5.
Liu, Xue, Yuanjian Chen, Tieqiang Zhao, et al.. (2016). Differential Regulatory Role of Soluble Klothos on Cardiac Fibrogenesis in Hypertension. American Journal of Hypertension. 29(10). 1140–1147. 19 indexed citations
6.
Pi, Min, Karan Kapoor, Rui-Song Ye, et al.. (2016). Evidence for Osteocalcin Binding and Activation of GPRC6A in β-Cells. Endocrinology. 157(5). 1866–1880. 91 indexed citations
7.
Pi, Min & L. Darryl Quarles. (2013). Novel Bone Endocrine Networks Integrating Mineral and Energy Metabolism. Current Osteoporosis Reports. 11(4). 391–399. 44 indexed citations
8.
Pi, Min & L. Darryl Quarles. (2011). GPRC6A regulates prostate cancer progression. The Prostate. 72(4). 399–409. 71 indexed citations
10.
Pi, Min, Abby L. Parrill, & L. Darryl Quarles. (2010). GPRC6A Mediates the Non-genomic Effects of Steroids. Journal of Biological Chemistry. 285(51). 39953–39964. 145 indexed citations
11.
Pi, Min, Ling Chen, Minzhao Huang, Qiang Luo, & L. Darryl Quarles. (2008). Parathyroid-specific interaction of the calcium-sensing receptor and Gαq. Kidney International. 74(12). 1548–1556. 12 indexed citations
12.
Pi, Min, Ling Chen, Minzhao Huang, et al.. (2008). GPRC6A Null Mice Exhibit Osteopenia, Feminization and Metabolic Syndrome. PLoS ONE. 3(12). e3858–e3858. 201 indexed citations
13.
Liu, Shiguang, Wen Tang, Jianping Zhou, et al.. (2006). Fibroblast Growth Factor 23 Is a Counter-Regulatory Phosphaturic Hormone for Vitamin D. Journal of the American Society of Nephrology. 17(5). 1305–1315. 504 indexed citations breakdown →
14.
Pi, Min & L. Darryl Quarles. (2005). Osteoblast calcium‐sensing receptor has characteristics of ANF/7TM receptors. Journal of Cellular Biochemistry. 95(6). 1081–1092. 20 indexed citations
15.
Tu, Qisheng, Min Pi, Gérard Karsenty, et al.. (2003). Rescue of the skeletal phenotype in CasR-deficient mice by transfer onto the Gcm2 null background. Journal of Clinical Investigation. 111(7). 1029–1037. 124 indexed citations
16.
Pi, Min, Sanford C. Garner, Patrick J. Flannery, Robert F. Spurney, & L. Darryl Quarles. (2000). Sensing of Extracellular Cations in CasR-deficient Osteoblasts. Journal of Biological Chemistry. 275(5). 3256–3263. 71 indexed citations
17.
Ogawa, Satoshi, Ryunosuke Yoshino, Kiyohiko Angata, et al.. (2000). The mitochondrial DNA of Dictyostelium discoideum: complete sequence, gene content and genome organization. Molecular and General Genetics MGG. 263(3). 514–519. 70 indexed citations
18.
Spurney, Robert F., Min Pi, Patrick J. Flannery, & L. Darryl Quarles. (1999). Aluminum is a weak agonist for the calcium-sensing receptor. Kidney International. 55(5). 1750–1758. 32 indexed citations
20.
Pi, Min, et al.. (1998). Characterization of a novel small RNA encoded by Dictyostelium discoideum mitochondrial DNA. Molecular and General Genetics MGG. 257(2). 124–131. 10 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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