G. Tibolla

19 papers receiving 1.1k citations

Peers

G. Tibolla
Comparison fields: 5 of 80
  • Surgery 490
  • Clinical Biochemistry 78
  • Endocrinology, Diabetes and Metabolism 172
  • Immunology 173
  • Cancer Research 83
Replace Patrizia Uboldi with:
Patrizia Uboldi Italy
Tiziana Sampietro Italy
Yumiko Nakagawa-Toyama Japan
Bernd Hewing Germany
Shozo Kobori Japan
Fabienne Nigon France
Takanori Nakajima Japan
Manal Zabalawi United States
Debi K. Swertfeger United States
Chien-Ping Liang United States
G. Tibolla relative to Patrizia Uboldi Italy Patrizia Uboldi's profile →
Citations per field
00.5×2.7×
Patrizia Uboldi · 1×
Citations per year

Countries citing papers authored by G. Tibolla

Since Specialization
Citations

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

Fields of papers citing papers by G. Tibolla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011218
2 2006149
3 2015137
4 201195
5 200893
6 201389
7 201684
8 200756
9 201452
10 200949
11 201436
12 201324
13 201322
14 201412
15 20159
16 20152
17 20171
18 20141
19 20141
20 20110

About G. Tibolla

G. Tibolla is a scholar working on Surgery, Molecular Biology, Endocrinology, Diabetes and Metabolism, Immunology and Oncology, having authored 21 papers that have together received 1.1k indexed citations. Recurring topics across this work include Lipoproteins and Cardiovascular Health (12 papers), Atherosclerosis and Cardiovascular Diseases (3 papers), Cancer, Lipids, and Metabolism (2 papers), Computational Drug Discovery Methods (2 papers), PI3K/AKT/mTOR signaling in cancer (2 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (2 papers), Natural Antidiabetic Agents Studies (1 paper) and Alzheimer's disease research and treatments (1 paper). The work is most often cited by research in Surgery (490 citations), Clinical Biochemistry (78 citations), Endocrinology, Diabetes and Metabolism (172 citations), Immunology (173 citations) and Cancer Research (83 citations). G. Tibolla has collaborated with scholars based in Italy, United Kingdom and Australia. Frequent co-authors include Alberico L. Catapano, Giuseppe Danilo Norata, Alberto Corsini, Nicola Ferri, Angela Pirillo, Angelo Poletti, Francesco Cipollone, Andrea Mezzetti, Roberto Artali and Fiorella Meneghetti. Their work appears in journals such as Atherosclerosis, Nutrition Metabolism and Cardiovascular Diseases, PLoS ONE, The Annual Review of Pharmacology and Toxicology and The Journal of Clinical Endocrinology & Metabolism.

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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