Mayumi Nagashimada

3.2k citations
35 papers · 2.6k indexed · 1 hit paper · h-index 26

Mayumi Nagashimada

35 papers receiving 2.6k citations

Hit Papers

SGLT2 Inhibition by Empagliflozin Promotes Fat Utilizatio...4022017202620202023100200300400

Peers

Mayumi Nagashimada
Comparison fields: 5 of 104
  • Endocrinology, Diabetes and Metabolism 602
  • Biochemistry 203
  • Epidemiology 962
  • Hepatology 197
  • Gastroenterology 133
Replace Yiming Lin with:
Yiming Lin China
Junta Imai Japan
Lone Hansen United States
Changting Xiao Canada
Ke Ma United States
Ludger Scheja Germany
Paul M. Titchenell United States
Wendell J. Lu United States
Peter A. Meléndez United States
Shojiro Sawada Japan
Mayumi Nagashimada relative to Yiming Lin China Yiming Lin's profile →
Citations per field
00.5×5.8×
Yiming Lin · 1×
Citations per year

Countries citing papers authored by Mayumi Nagashimada

Since Specialization
Citations

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

Fields of papers citing papers by Mayumi Nagashimada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20242
2 20232
3 202252
4 202141
5 202156
6 202057
7 201953
8 201929
9
SGLT2 Inhibition by Empagliflozin Promotes Fat Utilization and Browning and Attenuates Inflammation and Insulin Resistance by Polarizing M2 Macrophages in Diet-induced Obese Micebreakdown →
2017402
10 201667
11 201673
12 2015212
13 2015173
14 201463
15 201365
16
CCR5はマクロファージ動員とM1/M2状態を調節することで,肥満による脂肪組織の炎症およびインスリン抵抗性に重要な役割を果たす
20131
17 2012240
18 201283
19 2008132
20 200656

About Mayumi Nagashimada

Mayumi Nagashimada is a scholar working on Epidemiology, Endocrinology, Diabetes and Metabolism and Hepatology, having authored 35 papers that have together received 2.6k indexed citations. Recurring topics across this work include Liver Disease Diagnosis and Treatment (18 papers), Diet, Metabolism, and Disease (8 papers), Adipose Tissue and Metabolism (7 papers), Adipokines, Inflammation, and Metabolic Diseases (7 papers), Congenital gastrointestinal and neural anomalies (5 papers), Genomics, phytochemicals, and oxidative stress (4 papers), Liver Disease and Transplantation (3 papers) and Lipid metabolism and disorders (3 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (602 citations), Biochemistry (203 citations) and Epidemiology (962 citations). Mayumi Nagashimada has collaborated with scholars based in Japan, China and United States. Frequent co-authors include Tsuguhito Ota, Yinhua Ni, Shuichi Kaneko, Fen Zhuge, Naoto Nagata, Toshihiro Uesaka, Hideki Enomoto, Liang Xu, Guanliang Chen and Naofumi Mukaida. Their work appears in journals such as Journal of Clinical Investigation, Journal of Neuroscience and PLoS ONE.

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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