Maya Kudo

533 citations
12 papers · 381 · 1 hit paper · h-index 7

Impact in

Papers in

    • Adipose Tissue and Metabolism 4
    • Alzheimer's disease research and treatments 1
    • Liver Disease Diagnosis and Treatment 2
    • Adipokines, Inflammation, and Metabolic Diseases 2

Maya Kudo

12 papers receiving 367 citations

Maya Kudo's Hit Papers

Therapeutic Potential of Centella asiatica and Its Triterpenes: A Review 2020 · 218 citations
2180+2+4Years since publication50100150200

Peers

Maya Kudo
Comparison fields: 5 of 71
  • Complementary and alternative medicine 150
  • Drug Discovery 1
  • Endocrinology, Diabetes and Metabolism 77
  • Neurology 32
  • Pharmacology 43
Replace Misa Hayashi with:
Misa Hayashi Japan
Seok Yong Kang South Korea
Mixia Zhang China
In-Chan Seol South Korea
Ledyane Taynara Marton Brazil
Jungbin Song South Korea
Md Zakaria United Arab Emirates
Chenghao Yu China
Neeraj S. Vyawahare India
Maya Kudo relative to Misa Hayashi Japan Misa Hayashi's profile →
Citations per field
00.5×4.5×
Misa Hayashi · 1×
Citations per year

Countries citing papers authored by Maya Kudo

Since Specialization
Citations

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

Fields of papers citing papers by Maya Kudo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 18 scholars most cited alongside Maya Kudo, 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 Maya Kudo Line = papers co-authored together Maya Kudo links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1
Therapeutic Potential of Centella asiatica and Its Triterpenes: A Review
Hit paper breakdown →
2020218
2 201556
3 201735
4 201722
5 202115
6 202115
7 20226
8 20244
9 20203
10 20223
11 20223
12 20181

About Maya Kudo

Maya Kudo is a scholar working on Physiology, Epidemiology, Molecular Biology, Endocrine and Autonomic Systems and Complementary and alternative medicine, having authored 12 papers that have together received 381 indexed citations. Recurring topics across this work include Adipose Tissue and Metabolism (4 papers), Liver Disease Diagnosis and Treatment (2 papers), Adipokines, Inflammation, and Metabolic Diseases (2 papers), Regulation of Appetite and Obesity (2 papers), Alzheimer's disease research and treatments (1 paper), Diabetes and associated disorders (1 paper), Cholinesterase and Neurodegenerative Diseases (1 paper) and Diet, Metabolism, and Disease (1 paper). The work is most often cited by research in Complementary and alternative medicine (150 citations), Drug Discovery (1 citation), Endocrinology, Diabetes and Metabolism (77 citations), Neurology (32 citations) and Pharmacology (43 citations). Maya Kudo has collaborated with scholars based in Japan and China. Frequent co-authors include Ming Gao, Misa Hayashi, Tonghua Liu, Lingling Qin, Li Wu, Chengfei Zhang, You Wu, Kohtaro Minami, Susumu Seino and Harumi Takahashi. Their work appears in journals such as PLoS ONE, Biological and Pharmaceutical Bulletin, Journal of Nutritional Science, Food Science & Nutrition and Frontiers in Endocrinology.

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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