Fábio Hecht

1.3k citations
33 papers · 846 · h-index 15

Impact in

Papers in

Fábio Hecht

33 papers receiving 827 citations

Peers

Fábio Hecht
Comparison fields: 5 of 114
  • Endocrinology, Diabetes and Metabolism 205
  • Biochemistry 59
  • Cancer Research 113
  • Drug Discovery 1
  • Orthopedics and Sports Medicine 44
Replace Won Jun Lee with:
Won Jun Lee South Korea
Gursev S. Dhaunsi Kuwait
Huanhuan Xu China
Horng‐Mo Lee Taiwan
Chi Ming Wong Hong Kong
Hiroko Fujii Japan
Weijian Zhang China
Tuangporn Suthiphongchai Thailand
Hamid Soraya Iran
Fábio Hecht relative to Won Jun Lee South Korea Won Jun Lee's profile →
Citations per field
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Won Jun Lee · 1×
Citations per year

Countries citing papers authored by Fábio Hecht

Since Specialization
Citations

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

Fields of papers citing papers by Fábio Hecht

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 33 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2016216
2 201471
3 202360
4 202052
5 201351
6 202245
7 201641
8 201335
9 202128
10 201628
11 201628
12
Identification by fluorescence of two G rings: (46,XY,21r) G deletion syndrome I and (46, XX, 22r) G deletion syndrome II.
197226
13 202023
14 202121
15 201616
16 201813
17 201811
18 201611
19 20229
20 19719

About Fábio Hecht

Fábio Hecht is a scholar working on Molecular Biology, Endocrinology, Diabetes and Metabolism, Pathology and Forensic Medicine, Infectious Diseases and Epidemiology, having authored 33 papers that have together received 846 indexed citations. Recurring topics across this work include Thyroid Disorders and Treatments (6 papers), COVID-19 Clinical Research Studies (4 papers), Thyroid Cancer Diagnosis and Treatment (4 papers), Vitamin D Research Studies (3 papers), Redox biology and oxidative stress (3 papers), Antioxidant Activity and Oxidative Stress (3 papers), Glutathione Transferases and Polymorphisms (3 papers) and Cancer, Hypoxia, and Metabolism (3 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (205 citations), Biochemistry (59 citations), Cancer Research (113 citations), Drug Discovery (1 citation) and Orthopedics and Sports Medicine (44 citations). Fábio Hecht has collaborated with scholars based in Brazil, United States and France. Frequent co-authors include Denise Pires de Carvalho, Rodrigo S. Fortunato, Luciana B. Gentile, Doris Rosenthal, Andrea Cláudia Freitas Ferreira, Corinne Dupuy, Juliana Cazarin, Isaac S. Harris, Helton Estrela Ramos and Luiz Eurico Nasciutti. Their work appears in journals such as Endocrine Related Cancer, Thyroid, Oxidative Medicine and Cellular Longevity, PLoS ONE and Journal of Endocrinology.

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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