Maya Ramdas
Impact in
- Clinical Biochemistry top 1%
- Advanced Glycation End Products research
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- Natural Antidiabetic Agents Studies
- Diet, Metabolism, and Disease
- Diabetes, Cardiovascular Risks, and Lipoproteins
Papers in
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- Natural Antidiabetic Agents Studies 2
- Hormonal Regulation and Hypertension 2
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- Biochemical effects in animals 3
- Co-authors
- Gary E. Striker (3 shared papers)Li Zhu (3 shared papers)Helen Vlassara (3 shared papers)Weijing Cai (3 shared papers)Xue Chen (2 shared papers)Jaime Uribarri (2 shared papers)Susan Goodman (2 shared papers)Renata Pyzik (2 shared papers)
- Journals
- Hormone and Metabolic Research (2 papers)Canadian Journal of Physiology and Pharmacology (1 paper)Diabetes Care (1 paper)Amino Acids (1 paper)Cardiorenal Medicine (1 paper)
- Partner nations
- United StatesIsraelIndia
In The Last Decade
Maya Ramdas
8 papers receiving 643 citations
Peers
Comparison fields: 5 of 73
- Clinical Biochemistry 418
- Endocrinology, Diabetes and Metabolism 298
- Physiology 170
- Geriatrics and Gerontology 25
- Nutrition and Dietetics 88
Countries citing papers authored by Maya Ramdas
This map shows the geographic impact of Maya Ramdas'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 Ramdas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya Ramdas more than expected).
Fields of papers citing papers by Maya Ramdas
This network shows the impact of papers produced by Maya Ramdas. 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 Ramdas. The network helps show where Maya Ramdas may publish in the future.
Co-authors
The 19 scholars most cited alongside Maya Ramdas, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 272 | |
| 2 | 2012 | 262 | |
| 3 | 2013 | 61 | |
| 4 | 2014 | 18 | |
| 5 | 2012 | 13 | |
| 6 | 2018 | 11 | |
| 7 | 2014 | 11 | |
| 8 | 2016 | 2 |
About Maya Ramdas
Maya Ramdas is a scholar working on Endocrinology, Diabetes and Metabolism, Physiology, Molecular Biology, Clinical Biochemistry and Surgery, having authored 8 papers that have together received 650 indexed citations. Recurring topics across this work include Advanced Glycation End Products research (3 papers), Biochemical effects in animals (3 papers), Metabolism, Diabetes, and Cancer (2 papers), Natural Antidiabetic Agents Studies (2 papers), Hormonal Regulation and Hypertension (2 papers), Pancreatic function and diabetes (1 paper), MicroRNA in disease regulation (1 paper) and Endoplasmic Reticulum Stress and Disease (1 paper). The work is most often cited by research in Clinical Biochemistry (418 citations), Endocrinology, Diabetes and Metabolism (298 citations), Physiology (170 citations), Geriatrics and Gerontology (25 citations) and Nutrition and Dietetics (88 citations). Maya Ramdas has collaborated with scholars based in United States, Israel and India. Frequent co-authors include Gary E. Striker, Li Zhu, Helen Vlassara, Weijing Cai, Xue Chen, Jaime Uribarri, Susan Goodman, Renata Pyzik, Michal Armoni and Xue Chen. Their work appears in journals such as Hormone and Metabolic Research, Canadian Journal of Physiology and Pharmacology, Diabetes Care, Amino Acids and Cardiorenal Medicine.
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.