Jun Cai

660 citations
43 papers · 356 · h-index 11

Impact in

Papers in

Jun Cai

38 papers receiving 354 citations

Peers

Jun Cai
Comparison fields: 5 of 72
  • Nephrology 63
  • Pulmonary and Respiratory Medicine 174
  • Cardiology and Cardiovascular Medicine 96
  • Health Informatics 5
  • Endocrinology, Diabetes and Metabolism 41
Replace Subhashish Agarwal with:
Subhashish Agarwal United States
Masatsugu Kishida Japan
Alícia Traveset Spain
Magda Kusus Germany
Chiu‐Huang Kuo Taiwan
Jean‐Loup Machu France
Scott Sherman United States
John R. Montford United States
Carmen Jurjo Spain
Nicolas F. Schroten Netherlands
Jun Cai relative to Subhashish Agarwal United States Subhashish Agarwal's profile →
Citations per field
00.5×3.6×
Subhashish Agarwal · 1×
Citations per year

Countries citing papers authored by Jun Cai

Since Specialization
Citations

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

Fields of papers citing papers by Jun Cai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201937
2 201634
3 201233
4 201725
5 201723
6 201822
7 201917
8 202016
9 201912
10 201812
11 201712
12 20209
13 20229
14 20189
15 20207
16 20197
17 20197
18 20207
19 20206
20 20206

About Jun Cai

Jun Cai is a scholar working on Pulmonary and Respiratory Medicine, Cardiology and Cardiovascular Medicine, Endocrinology, Diabetes and Metabolism, Surgery and Epidemiology, having authored 43 papers that have together received 356 indexed citations. Recurring topics across this work include Vasculitis and related conditions (15 papers), Hormonal Regulation and Hypertension (9 papers), Blood Pressure and Hypertension Studies (8 papers), Renal and Vascular Pathologies (6 papers), Coronary Artery Anomalies (4 papers), Adrenal and Paraganglionic Tumors (4 papers), Cardiovascular Health and Disease Prevention (3 papers) and Adrenal Hormones and Disorders (3 papers). The work is most often cited by research in Nephrology (63 citations), Pulmonary and Respiratory Medicine (174 citations), Cardiology and Cardiovascular Medicine (96 citations), Health Informatics (5 citations) and Endocrinology, Diabetes and Metabolism (41 citations). Jun Cai has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Huimin Zhang, Xianliang Zhou, Dan Qi, Lei Song, Peng Fan, Shouling Wu, Haiying Wu, Deyu Zheng, Lirui Yang and Kun‐Qi Yang. Their work appears in journals such as The American Journal of the Medical Sciences, International Journal of Cardiology, Circulation Journal, Journal of Hypertension and The Heart Surgery Forum.

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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