Fernando Abdulkader
- Surgery top 5%
- Molecular Biology
- Endocrinology, Diabetes and Metabolism top 5%
- Physiology top 10%
- Genetics top 10%
- Co-authors
- Rui CuriPatrik RorsmanÂngelo Rafael CarpinelliEduardo RebelatoJoaquim ProcópioJuris GalvanovskisMatthias BraunLena Eliasson
- Topics
- Pancreatic function and diabetes (16 papers)Lipid Membrane Structure and Behavior (6 papers)Metabolism, Diabetes, and Cancer (6 papers)
- Partner nations
- BrazilUnited KingdomSweden
In The Last Decade
Fernando Abdulkader
34 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 124
- Surgery 590
- Molecular Biology 450
- Endocrinology, Diabetes and Metabolism 344
- Physiology 249
- Genetics 217
Countries citing papers authored by Fernando Abdulkader
This map shows the geographic impact of Fernando Abdulkader'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 Fernando Abdulkader with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fernando Abdulkader more than expected).
Fields of papers citing papers by Fernando Abdulkader
This network shows the impact of papers produced by Fernando Abdulkader. 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 Fernando Abdulkader. The network helps show where Fernando Abdulkader may publish in the future.
Co-authorship network of co-authors of Fernando Abdulkader
This figure shows the co-authorship network connecting the top 25 collaborators of Fernando Abdulkader. A scholar is included among the top collaborators of Fernando Abdulkader based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fernando Abdulkader. Fernando Abdulkader is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | 7 | |
| 3 | 3 | |
| 4 | 24 | |
| 5 | 21 | |
| 6 | 114 | |
| 7 | 53 | |
| 8 | GLP-1 inhibits glucagon secretion from human alpha cells by a direct effect | 1 |
| 9 | 7 | |
| 10 | 26 | |
| 11 | A mouse model of neonatal diabetes caused by the K-ATP channel mutation Kir6.2-V59M | 3 |
| 12 | 89 | |
| 13 | 74 | |
| 14 | 141 | |
| 15 | 67 | |
| 16 | 5 | |
| 17 | 54 | |
| 18 | 22 | |
| 19 | 23 | |
| 20 | 4 |
About Fernando Abdulkader
Fernando Abdulkader is a scholar working on Electrochemistry, Endocrinology, Diabetes and Metabolism and Clinical Biochemistry, having authored 34 papers that have together received 1.2k indexed citations. Recurring topics across this work include Pancreatic function and diabetes (16 papers), Lipid Membrane Structure and Behavior (6 papers) and Metabolism, Diabetes, and Cancer (6 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (344 citations), Surgery (590 citations) and Nutrition and Dietetics (154 citations). Fernando Abdulkader has collaborated with scholars based in Brazil, United Kingdom and Sweden. Frequent co-authors include Rui Curi, Patrik Rorsman, Ângelo Rafael Carpinelli, Eduardo Rebelato, Joaquim Procópio, Juris Galvanovskis, Matthias Braun, Lena Eliasson, Manoel Arcísio-Miranda and Michael B. Hoppa. Their work appears in journals such as Journal of Clinical Investigation, PLoS ONE and The Journal of Physiology.
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.