Pan Pan

2.9k citations
60 papers · 1.7k indexed · h-index 19

Impact in

    • SARS-CoV-2 and COVID-19 Research
    • COVID-19 Clinical Research Studies
    • Viral Infections and Vectors
  • Immunology top 5%
    • interferon and immune responses

Papers in

Pan Pan

57 papers receiving 1.7k citations

Peers

Pan Pan
Comparison fields: 5 of 121
  • Infectious Diseases 535
  • Immunology 468
  • Biological Psychiatry 33
  • Molecular Biology 842
  • Neurology 138
Replace Prasun K. Datta with:
Prasun K. Datta United States
Xinran Li China
Lillian Zalduondo United States
Luciana P. Tavares Brazil
Balaji Banoth United States
Prashanta Silwal South Korea
Seungwha Paik South Korea
Bárbara N. Porto Brazil
Andrea Schroll Austria
Sebastian Weis Germany
Pan Pan relative to Prasun K. Datta United States Prasun K. Datta's profile →
Citations per field
00.5×5.3×
Prasun K. Datta · 1×
Citations per year

Countries citing papers authored by Pan Pan

Since Specialization
Citations

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

Fields of papers citing papers by Pan Pan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Pan Pan, 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 Pan Pan Line = papers co-authored together Pan Pan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20253
3 20242
4 20245
5 20244
6 20241
7 20235
8 202312
9 202348
10 202243
11 202218
12 2021154
13 202148
14 20211
15 20213
16 202063
17 201912
18 20194
19 2018162
20 201610

About Pan Pan

Pan Pan is a scholar working on Infectious Diseases, General Engineering, Immunology, Molecular Medicine and Critical Care and Intensive Care Medicine, having authored 60 papers that have together received 1.7k indexed citations. Recurring topics across this work include Inflammasome and immune disorders (11 papers), interferon and immune responses (9 papers), SARS-CoV-2 and COVID-19 Research (8 papers), COVID-19 Clinical Research Studies (6 papers), Viral Infections and Vectors (5 papers), Mosquito-borne diseases and control (4 papers), Sepsis Diagnosis and Treatment (4 papers) and Long-Term Effects of COVID-19 (4 papers). The work is most often cited by research in Infectious Diseases (535 citations), Immunology (468 citations), Biological Psychiatry (33 citations), Molecular Biology (842 citations) and Neurology (138 citations). Pan Pan has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Jianguo Wu, Wenbiao Wang, Mingfu Tian, Kailang Wu, Feng Xiao, Dingwen Hu, Zhen Luo, Keli Chen, Lang Rao and Pin Wan. Their work appears in journals such as Nature Communications, Frontiers in Cellular and Infection Microbiology, Frontiers in Medicine, Viruses and Journal of Medical Virology.

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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