Chan Dai
Impact in
- Infectious Diseases top 10%
- COVID-19 Clinical Research Studies
- SARS-CoV-2 and COVID-19 Research
- SARS-CoV-2 detection and testing
- Modeling and Simulation top 10%
- COVID-19 epidemiological studies
Papers in
-
- IL-33, ST2, and ILC Pathways 4
- Psoriasis: Treatment and Pathogenesis 3
- Immune Cell Function and Interaction 3
- Surgery 2
- Eosinophilic Esophagitis 2
- Co-authors
- Fanfan Zeng (3 shared papers)Fang Zheng (1 shared paper)Zheng Wang (1 shared paper)Lin Wang (2 shared papers)Jianyu Li (1 shared paper)Lei Xu (1 shared paper)Pengcheng Cai (2 shared papers)Jinbiao Wang (3 shared papers)
- Journals
- Clinical Cosmetic and Investigational Dermatology (2 papers)Brain Behavior and Immunity (1 paper)International Journal of Biological Macromolecules (1 paper)Neuroscience Bulletin (1 paper)Frontiers in Immunology (1 paper)
- Partner nations
- ChinaSouth Korea
In The Last Decade
Chan Dai
10 papers receiving 276 citations
Peers
Comparison fields: 5 of 72
- Infectious Diseases 158
- Modeling and Simulation 31
- Obstetrics and Gynecology 38
- Neurology 55
- Immunology 50
Countries citing papers authored by Chan Dai
This map shows the geographic impact of Chan Dai'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 Chan Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chan Dai more than expected).
Fields of papers citing papers by Chan Dai
This network shows the impact of papers produced by Chan Dai. 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 Chan Dai. The network helps show where Chan Dai may publish in the future.
Co-authors
The 25 scholars most cited alongside Chan Dai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 208 | |
| 2 | 2023 | 28 | |
| 3 | 2022 | 14 | |
| 4 | 2024 | 9 | |
| 5 | 2021 | 8 | |
| 6 | 2023 | 4 | |
| 7 | 2024 | 3 | |
| 8 | 2024 | 2 | |
| 9 | 2023 | 2 | |
| 10 | 2023 | 2 |
About Chan Dai
Chan Dai is a scholar working on Immunology, Surgery, Molecular Biology, Pathology and Forensic Medicine and Infectious Diseases, having authored 10 papers that have together received 280 indexed citations. Recurring topics across this work include IL-33, ST2, and ILC Pathways (4 papers), Psoriasis: Treatment and Pathogenesis (3 papers), Immune Cell Function and Interaction (3 papers), Eosinophilic Esophagitis (2 papers), Vitamin D Research Studies (2 papers), Long-Term Effects of COVID-19 (1 paper), Neuroinflammation and Neurodegeneration Mechanisms (1 paper) and SARS-CoV-2 and COVID-19 Research (1 paper). The work is most often cited by research in Infectious Diseases (158 citations), Modeling and Simulation (31 citations), Obstetrics and Gynecology (38 citations), Neurology (55 citations) and Immunology (50 citations). Chan Dai has collaborated with scholars based in China and South Korea. Frequent co-authors include Fanfan Zeng, Fang Zheng, Zheng Wang, Lin Wang, Jianyu Li, Lei Xu, Pengcheng Cai, Jinbiao Wang, Zheng Tan and Yong Xu. Their work appears in journals such as Clinical Cosmetic and Investigational Dermatology, Brain Behavior and Immunity, International Journal of Biological Macromolecules, Neuroscience Bulletin and Frontiers in Immunology.
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.