David E. Curtis
Impact in
- Surgery top 2%
- Lipoproteins and Cardiovascular Health
- Cholesterol and Lipid Metabolism
- Cancer Research top 10%
- Cancer, Lipids, and Metabolism
Papers in
- Surgery 3
- Lipoproteins and Cardiovascular Health 2
- Cholesterol and Lipid Metabolism 2
- Bariatric Surgery and Outcomes 1
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- Protein Kinase Regulation and GTPase Signaling 1
- Nuclear Structure and Function 1
- Co-authors
- Robert E. Hammer (3 shared papers)Jay D. Horton (3 shared papers)Rita Garuti (2 shared papers)Y K Ho (2 shared papers)Norma N. Anderson (2 shared papers)Shirya Rashid (2 shared papers)Yuriy K. Bashmakov (1 shared paper)Young-Ah Moon (1 shared paper)
- Journals
- Obesity Surgery (1 paper)Proceedings of the National Academy of Sciences (1 paper)Cell Metabolism (1 paper)Journal of Clinical Investigation (1 paper)
- Partner nations
- United StatesSouth KoreaPoland
In The Last Decade
David E. Curtis
4 papers receiving 1.3k citations
David E. Curtis's Hit Papers
Peers
Comparison fields: 5 of 65
- Surgery 874
- Cancer Research 182
- Biochemistry 92
- Endocrinology, Diabetes and Metabolism 103
- Computational Theory and Mathematics 96
Countries citing papers authored by David E. Curtis
This map shows the geographic impact of David E. Curtis'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 David E. Curtis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David E. Curtis more than expected).
Fields of papers citing papers by David E. Curtis
This network shows the impact of papers produced by David E. Curtis. 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 David E. Curtis. The network helps show where David E. Curtis may publish in the future.
Co-authors
The 23 scholars most cited alongside David E. Curtis, 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 | Decreased plasma cholesterol and hypersensitivity to statins in mice lacking Pcsk9 Hit paper breakdown → | 2005 | 571 |
| 2 | Secreted PCSK9 decreases the number of LDL receptors in hepatocytes and inlivers of parabiotic mice Hit paper breakdown → | 2006 | 551 |
| 3 | 2009 | 187 | |
| 4 | 2009 | 14 |
About David E. Curtis
David E. Curtis is a scholar working on Surgery, Molecular Biology, Endocrinology, Diabetes and Metabolism, Physiology and Biochemistry, having authored 4 papers that have together received 1.3k indexed citations. Recurring topics across this work include Lipoproteins and Cardiovascular Health (2 papers), Cholesterol and Lipid Metabolism (2 papers), Protein Kinase Regulation and GTPase Signaling (1 paper), Bariatric Surgery and Outcomes (1 paper), Nuclear Structure and Function (1 paper), Diabetes Treatment and Management (1 paper), Adipose Tissue and Metabolism (1 paper) and Lipid metabolism and biosynthesis (1 paper). The work is most often cited by research in Surgery (874 citations), Cancer Research (182 citations), Biochemistry (92 citations), Endocrinology, Diabetes and Metabolism (103 citations) and Computational Theory and Mathematics (96 citations). David E. Curtis has collaborated with scholars based in United States, South Korea and Poland. Frequent co-authors include Robert E. Hammer, Jay D. Horton, Rita Garuti, Y K Ho, Norma N. Anderson, Shirya Rashid, Yuriy K. Bashmakov, Young-Ah Moon, Thomas A. Lagace and Sahng Wook Park. Their work appears in journals such as Obesity Surgery, Proceedings of the National Academy of Sciences, Cell Metabolism and Journal of Clinical Investigation.
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.