Min Zhu

2.5k total citations
55 papers, 2.0k citations indexed

About

Min Zhu is a scholar working on Molecular Biology, Physiology and Surgery. According to data from OpenAlex, Min Zhu has authored 55 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 17 papers in Physiology and 16 papers in Surgery. Recurrent topics in Min Zhu's work include Pancreatic function and diabetes (16 papers), Metabolism, Diabetes, and Cancer (10 papers) and Adipose Tissue and Metabolism (9 papers). Min Zhu is often cited by papers focused on Pancreatic function and diabetes (16 papers), Metabolism, Diabetes, and Cancer (10 papers) and Adipose Tissue and Metabolism (9 papers). Min Zhu collaborates with scholars based in United States, China and Japan. Min Zhu's co-authors include Donald K. Ingram, Rafael de Cabo, Mark A. Lane, George S. Roth, Kenji Shima, Jacek Mamczarz, Sige Zou, Michael S. Lan, Toshiaki Sano and Akira Mizuno and has published in prestigious journals such as Journal of Biological Chemistry, The Journal of Immunology and Diabetes.

In The Last Decade

Min Zhu

54 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Min Zhu United States 23 872 683 361 307 294 55 2.0k
Tsuyoshi Kayo Japan 22 791 0.9× 1.1k 1.6× 657 1.8× 416 1.4× 282 1.0× 30 2.8k
Diana C. Albarado United States 14 1.1k 1.3× 1.2k 1.7× 206 0.6× 116 0.4× 636 2.2× 19 2.6k
Vijay Yechoor United States 28 968 1.1× 1.1k 1.7× 705 2.0× 193 0.6× 461 1.6× 61 2.8k
Yoshikazu Higami Japan 32 1.6k 1.8× 1.3k 1.9× 223 0.6× 718 2.3× 611 2.1× 160 3.5k
Jorge Suárez United States 26 1.0k 1.2× 1.6k 2.4× 224 0.6× 226 0.7× 219 0.7× 50 3.2k
Joseph M. Dhahbi United States 29 938 1.1× 1.6k 2.4× 153 0.4× 703 2.3× 157 0.5× 44 2.7k
Katie C. Coate United States 17 636 0.7× 1.1k 1.6× 413 1.1× 71 0.2× 338 1.1× 37 2.1k
Sebastian Brandhorst United States 24 2.4k 2.7× 1.2k 1.8× 128 0.4× 402 1.3× 315 1.1× 33 3.8k
Andrej Podlutsky United States 25 669 0.8× 1.1k 1.6× 112 0.3× 325 1.1× 205 0.7× 39 2.4k
Jennifer Jager France 20 1.2k 1.3× 1.1k 1.6× 298 0.8× 205 0.7× 805 2.7× 33 3.0k

Countries citing papers authored by Min Zhu

Since Specialization
Citations

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

Fields of papers citing papers by Min Zhu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Min Zhu

This figure shows the co-authorship network connecting the top 25 collaborators of Min Zhu. A scholar is included among the top collaborators of Min Zhu 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 Zhu. Min Zhu 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.
Yamaguchi, Yûko, Min Zhu, Ruin Moaddel, et al.. (2023). Relationships of GDF8 and 11 and Their Antagonists With Decline of Grip Strength Among Older Adults in the Baltimore Longitudinal Study of Aging. The Journals of Gerontology Series A. 78(10). 1793–1798.
2.
Li, Xiaoyang, Ruiqi Sun, Min Zhu, et al.. (2021). The glycosyltransferase ST3GAL2 modulates virus proliferation and the inflammation response in porcine reproductive and respiratory syndrome virus infection. Archives of Virology. 166(10). 2723–2732. 2 indexed citations
3.
Shi, Jingxuan, Heng Chi, Aiping Cao, et al.. (2021). Development of IMBs–qPCR detection method for Yersinia enterocolitica based on the foxA gene. Archives of Microbiology. 203(7). 4653–4662. 1 indexed citations
4.
Zhu, Min, Xiaoyang Li, Ruiqi Sun, et al.. (2021). The C/EBPβ-Dependent Induction of TFDP2 Facilitates Porcine Reproductive and Respiratory Syndrome Virus Proliferation. Virologica Sinica. 36(6). 1341–1351. 7 indexed citations
5.
Wu, Jiaqi, Heng Chi, Aiping Cao, et al.. (2020). The antiviral protein viperin interacts with the viral N protein to inhibit proliferation of porcine epidemic diarrhea virus. Archives of Virology. 165(10). 2279–2289. 11 indexed citations
6.
Cao, Aiping, Heng Chi, Jingxuan Shi, et al.. (2020). Visual Detection of Clostridium perfringens Alpha Toxin by Combining Nanometer Microspheres with Smart Phones. Microorganisms. 8(12). 1865–1865. 3 indexed citations
7.
Shi, Peidian, Yanxin Su, Min Zhu, et al.. (2020). SUMOylation of DDX39A Alters Binding and Export of Antiviral Transcripts to Control Innate Immunity. The Journal of Immunology. 205(1). 168–180. 25 indexed citations
8.
Hua, Deping, Jingxuan Shi, Zheng Tan, et al.. (2020). Porcine Immunoglobulin Fc Fused P30/P54 Protein of African Swine Fever Virus Displaying on Surface of S. cerevisiae Elicit Strong Antibody Production in Swine. Virologica Sinica. 36(2). 207–219. 20 indexed citations
9.
Zhu, Min, et al.. (2020). Effects of high-intensity interval training on adipose tissue lipolysis, inflammation, and metabolomics in aged rats. Pflügers Archiv - European Journal of Physiology. 472(2). 245–258. 27 indexed citations
10.
11.
12.
Li, Fanghui, Min Zhu, Rui Duan, et al.. (2018). Beneficial Autophagic Activities, Mitochondrial Function, and Metabolic Phenotype Adaptations Promoted by High-Intensity Interval Training in a Rat Model. Frontiers in Physiology. 9. 571–571. 29 indexed citations
13.
14.
Zhu, Min, Joanne Allard, Yongqing Zhang, et al.. (2014). Age-Related Brain Expression and Regulation of the Chemokine CCL4/MIP-1β in APP/PS1 Double-Transgenic Mice. Journal of Neuropathology & Experimental Neurology. 73(4). 362–374. 50 indexed citations
15.
Zhu, Min, Garrick D. Lee, Jingping Hu, et al.. (2007). Adipogenic signaling in rat white adipose tissue: Modulation by aging and calorie restriction. Experimental Gerontology. 42(8). 733–744. 62 indexed citations
16.
Ingram, Donald K., Min Zhu, Jacek Mamczarz, et al.. (2006). Calorie restriction mimetics: an emerging research field. Aging Cell. 5(2). 97–108. 321 indexed citations
17.
Zhu, Min, Rafael de Cabo, Mark A. Lane, & Donald K. Ingram. (2004). Caloric Restriction Modulates Early Events in Insulin Signaling in Liver and Skeletal Muscle of Rat. Annals of the New York Academy of Sciences. 1019(1). 448–452. 12 indexed citations
18.
Breslin, Mary B., Min Zhu, & Michael S. Lan. (2003). NeuroD1/E47 Regulates the E-box Element of a Novel Zinc Finger Transcription Factor, IA-1, in Developing Nervous System. Journal of Biological Chemistry. 278(40). 38991–38997. 79 indexed citations
19.
Zhu, Min, Akira Mizuno, Yoshihiko Noma, et al.. (2000). Defective Morphogenesis and Functional Maturation in Fetal Islet‐Like Cell Clusters From OLETF Rat, A Model of NIDDM. Journal of Diabetes Research. 1(4). 289–298. 2 indexed citations
20.
Shima, Kenji, Min Zhu, Yoshihiko Noma, et al.. (1997). Exercise training in Otsuka Long-Evans Tokushima Fatty rat, a model of spontaneous non-insulin-dependent diabetes mellitus: effects on the B-cell mass, insulin content and fibrosis in the pancreas. Diabetes Research and Clinical Practice. 35(1). 11–19. 22 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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