Ming Shi

1.6k citations
22 papers · 551 · h-index 10

Impact in

  • Nephrology top 10%
    • Dialysis and Renal Disease Management
    • COVID-19 Clinical Research Studies
    • SARS-CoV-2 and COVID-19 Research

Papers in

Ming Shi

20 papers receiving 538 citations

Peers

Ming Shi
Comparison fields: 5 of 77
  • Nephrology 77
  • Infectious Diseases 194
  • Neurology 105
  • Oncology 140
  • Geriatrics and Gerontology 15
Replace Hua Shui with:
Hua Shui China
Zsolt Fejes Hungary
Matthias Herrmann Germany
Lucía Tejedor-Santamaria Spain
Monika Pazgan−Simon Poland
Song Tong China
Lusia Sepiashvili Canada
Mengduan Liu China
Alfonso Amore Italy
Jeremy S. Leventhal United States
Ming Shi relative to Hua Shui China Hua Shui's profile →
Citations per field
00.5×
Hua Shui · 1×
Citations per year

Countries citing papers authored by Ming Shi

Since Specialization
Citations

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

Fields of papers citing papers by Ming Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ming Shi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ming Shi Line = papers co-authored together Ming Shi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020190
2 2006109
3 200756
4 201532
5 202030
6 200627
7 201925
8 202017
9 200411
10 202010
11 20239
12 20078
13 20178
14 20235
15 20135
16
[Angiotensin II-induced podocyte apoptosis: role of the MAPK subtypes].
20043
17
[Study on the inducible expression of toll-like receptors in human bladder cell line].
20043
18 20241
19
[Sphingosine kinase 1 promotes glioma cell proliferation under hypoxia via calcium signaling].
20151
20 20201

About Ming Shi

Ming Shi is a scholar working on Molecular Biology, Nephrology, Neurology, Oncology and Infectious Diseases, having authored 22 papers that have together received 551 indexed citations. Recurring topics across this work include Long-Term Effects of COVID-19 (4 papers), COVID-19 Clinical Research Studies (3 papers), Protein Kinase Regulation and GTPase Signaling (3 papers), Endoplasmic Reticulum Stress and Disease (2 papers), HER2/EGFR in Cancer Research (2 papers), Renal Diseases and Glomerulopathies (2 papers), Immune Response and Inflammation (2 papers) and Monoclonal and Polyclonal Antibodies Research (2 papers). The work is most often cited by research in Nephrology (77 citations), Infectious Diseases (194 citations), Neurology (105 citations), Oncology (140 citations) and Geriatrics and Gerontology (15 citations). Ming Shi has collaborated with scholars based in China and United States. Frequent co-authors include Zheng Yang, David A. Foster, Yingjie Shen, Hui Li, Alfredo Toschi, Vanessa Rodrik-Outmezguine, Chun Zhang, Jing Liu, Hui Tang and Fei Xiong. Their work appears in journals such as Frontiers in Pharmacology, Journal of Cellular Physiology, BMC Cancer, Acta Pharmacologica Sinica and Journal of Environmental Science and Health Part B.

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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