Jun Lei

1.1k citations
16 papers · 991 · h-index 14

Impact in

  • Nephrology top 2%
    • Chronic Kidney Disease and Diabetes
    • Renal Diseases and Glomerulopathies
    • Dialysis and Renal Disease Management
    • Hormonal Regulation and Hypertension
    • Menopause: Health Impacts and Treatments

Papers in

Jun Lei

16 papers receiving 976 citations

Peers

Jun Lei
Comparison fields: 5 of 76
  • Nephrology 186
  • Endocrinology, Diabetes and Metabolism 177
  • Reproductive Medicine 65
  • Genetics 202
  • Cancer Research 71
Replace Kunihiko Aya with:
Kunihiko Aya Japan
Thierry Gilbert France
Li-Wen Lai United States
Masataka Adachi Japan
R G Gronwald United States
Mark R. Hughes United States
Carlos Henrique de Lemos Muller Brazil
Maria Aminoff Finland
Jin‐Wei He China
Abhishek Aggarwal United States
Jun Lei relative to Kunihiko Aya Japan Kunihiko Aya's profile →
Citations per field
00.5×3.1×
Kunihiko Aya · 1×
Citations per year

Countries citing papers authored by Jun Lei

Since Specialization
Citations

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

Fields of papers citing papers by Jun Lei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

16 of 16 papers shown
#Work
1 1997191
2 1996119
3 200278
4 200075
5 199869
6 199967
7 200162
8 199961
9 200261
10 200460
11 199852
12 201148
13 199722
14 201218
15 19935
16 20193

About Jun Lei

Jun Lei is a scholar working on Molecular Biology, Immunology and Allergy, Nephrology, Genetics and Pharmacology, having authored 16 papers that have together received 991 indexed citations. Recurring topics across this work include Cell Adhesion Molecules Research (4 papers), TGF-β signaling in diseases (3 papers), Estrogen and related hormone effects (2 papers), Chronic Kidney Disease and Diabetes (2 papers), Inflammatory mediators and NSAID effects (2 papers), Pregnancy and Medication Impact (2 papers), Angiogenesis and VEGF in Cancer (1 paper) and Epigenetics and DNA Methylation (1 paper). The work is most often cited by research in Nephrology (186 citations), Endocrinology, Diabetes and Metabolism (177 citations), Reproductive Medicine (65 citations), Genetics (202 citations) and Cancer Research (71 citations). Jun Lei has collaborated with scholars based in United States, China and Belgium. Frequent co-authors include Joel Neugarten, Sharon Silbiger, Christian Rohlff, Flavia Borellini, Robert I. Glazer, Shakeel Ahmad, Fuad N. Ziyadeh, Marthe J. Howard, Anjali Acharya and Qing Ding. Their work appears in journals such as Kidney International, American Journal of Physiology-Renal Physiology, Development, Journal of Biological Chemistry and Mammalian Genome.

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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