Kai Mao

4.1k citations
86 papers · 2.8k indexed · h-index 31

Impact in

  • Aging top 2%
    • Genetics, Aging, and Longevity in Model Organisms
  • Physiology top 2%
    • Calcium signaling and nucleotide metabolism

Papers in

Kai Mao

82 papers receiving 2.8k citations

Peers

Kai Mao
Comparison fields: 5 of 129
  • Aging 137
  • Physiology 159
  • Epidemiology 1.0k
  • Cell Biology 453
  • Cancer Research 374
Replace Chengqun Huang with:
Chengqun Huang United States
Mukesh K. Jain United States
Muriel Laffargue France
Mehboob A. Hussain United States
Chanhee Kang South Korea
Javier Rodríguez‐Ubreva Spain
Tomomi Ueyama Japan
Christina N. Bennett United States
Xiang Ao China
Albert R. Davalos United States
Kai Mao relative to Chengqun Huang United States Chengqun Huang's profile →
Citations per field
00.5×3.4×
Chengqun Huang · 1×
Citations per year

Countries citing papers authored by Kai Mao

Since Specialization
Citations

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

Fields of papers citing papers by Kai Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 20249
2 20242
3 20242
4 20240
5 202314
6 20234
7 20231
8 202216
9 20225
10 202030
11 20191
12 201951
13 201531
14 201555
15 201463
16 201129
17
Pest status and control options for termites (Isoptera) in the Luhya Community of Western Kenya.
20104
18
Assessment of Genetic Diversity of Native Bermudagrass Accessions Collected in Panxi Region,Sichuan Province
20040
19
Soil substrate effects on cold resistance of Emei Eremochloa ophiuroides
20042
20
Studies on centipedegrass
20001

About Kai Mao

Kai Mao is a scholar working on Aging, Cancer Research, Hepatology, Physiology and Oncology, having authored 86 papers that have together received 2.8k indexed citations. Recurring topics across this work include Autophagy in Disease and Therapy (14 papers), Hepatocellular Carcinoma Treatment and Prognosis (9 papers), Cholangiocarcinoma and Gallbladder Cancer Studies (9 papers), Cancer, Lipids, and Metabolism (6 papers), Endoplasmic Reticulum Stress and Disease (6 papers), RNA modifications and cancer (6 papers), Mitochondrial Function and Pathology (6 papers) and Breast Cancer Treatment Studies (6 papers). The work is most often cited by research in Aging (137 citations), Physiology (159 citations), Epidemiology (1.0k citations), Cell Biology (453 citations) and Cancer Research (374 citations). Kai Mao has collaborated with scholars based in China, United States and Canada. Frequent co-authors include Daniel J. Klionsky, Ke Wang, Xu Liu, Paul A. Volden, Satoru Kobayashi, Qiangrong Liang, Xianmin Xu, Derek Timm, Zhiyu Xiao and Jieqiong Liu. Their work appears in journals such as Autophagy, Proceedings of the National Academy of Sciences, Journal of Biological Chemistry, International Journal of Surgery and GeroScience.

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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