Mário J.A. Saad

2.2k total citations
51 papers, 1.7k citations indexed

About

Mário J.A. Saad is a scholar working on Molecular Biology, Physiology and Surgery. According to data from OpenAlex, Mário J.A. Saad has authored 51 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Molecular Biology, 19 papers in Physiology and 11 papers in Surgery. Recurrent topics in Mário J.A. Saad's work include Metabolism, Diabetes, and Cancer (15 papers), Adipose Tissue and Metabolism (15 papers) and Pancreatic function and diabetes (11 papers). Mário J.A. Saad is often cited by papers focused on Metabolism, Diabetes, and Cancer (15 papers), Adipose Tissue and Metabolism (15 papers) and Pancreatic function and diabetes (11 papers). Mário J.A. Saad collaborates with scholars based in Brazil, United States and Italy. Mário J.A. Saad's co-authors include Franco Folli, C. Ronald Kahn, Lı́cio A. Velloso, Antônio C. Boschero, José Barreto Campello Carvalheira, Everardo M. Carneiro, Alexandre G. Oliveira, Eduardo Melani Rocha, Tiago Gomes Araújo and LA Velloso and has published in prestigious journals such as New England Journal of Medicine, Journal of Clinical Investigation and Circulation Research.

In The Last Decade

Mário J.A. Saad

51 papers receiving 1.7k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mário J.A. Saad Brazil 24 689 604 392 311 249 51 1.7k
Aldo V. Greco Italy 28 658 1.0× 866 1.4× 448 1.1× 564 1.8× 220 0.9× 60 2.3k
Natalia Rudovich Germany 25 517 0.8× 928 1.5× 584 1.5× 264 0.8× 261 1.0× 57 1.9k
David D’Alessio United States 15 553 0.8× 754 1.2× 718 1.8× 265 0.9× 341 1.4× 20 1.7k
Frank Isken Germany 17 435 0.6× 403 0.7× 438 1.1× 141 0.5× 179 0.7× 27 1.2k
Mark E. Cleasby United Kingdom 23 828 1.2× 1.0k 1.7× 283 0.7× 242 0.8× 86 0.3× 33 2.1k
Alberto O. Chávez United States 18 731 1.1× 539 0.9× 273 0.7× 312 1.0× 82 0.3× 26 1.6k
Marek Strączkowski Poland 26 827 1.2× 927 1.5× 355 0.9× 183 0.6× 288 1.2× 76 2.4k
Anne Marie Salapatek Canada 23 846 1.2× 772 1.3× 513 1.3× 803 2.6× 196 0.8× 49 2.3k
Marcos C. Carreira Spain 20 536 0.8× 923 1.5× 321 0.8× 200 0.6× 582 2.3× 39 2.0k
Joanna E. Chivers United Kingdom 10 642 0.9× 585 1.0× 137 0.3× 204 0.7× 302 1.2× 11 2.1k

Countries citing papers authored by Mário J.A. Saad

Since Specialization
Citations

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

Fields of papers citing papers by Mário J.A. Saad

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mário J.A. Saad. 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 Mário J.A. Saad. The network helps show where Mário J.A. Saad may publish in the future.

Co-authorship network of co-authors of Mário J.A. Saad

This figure shows the co-authorship network connecting the top 25 collaborators of Mário J.A. Saad. A scholar is included among the top collaborators of Mário J.A. Saad 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 Mário J.A. Saad. Mário J.A. Saad is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Stein, Angélica Miki, et al.. (2020). Effects of a four week detraining period on physical, metabolic, and inflammatory profiles of elderly women who regularly participate in a program of strength training. European Review of Aging and Physical Activity. 17(1). 12–12. 12 indexed citations
2.
Júnior, José Carlos de Lima, Simone van de Sande‐Lee, Joseane Morari, et al.. (2017). Impairment of body mass reduction-associated activation of brown/beige adipose tissue in patients with type 2 diabetes mellitus. International Journal of Obesity. 41(11). 1662–1668. 12 indexed citations
3.
Zanotto, Tamires M., Paula G.F. Quaresma, Dioze Guadagnini, et al.. (2016). Blocking iNOS and endoplasmic reticulum stress synergistically improves insulin resistance in mice. Molecular Metabolism. 6(2). 206–218. 28 indexed citations
4.
Saad, Mário J.A., et al.. (2016). Acanthosis Nigricans and Insulin Resistance. New England Journal of Medicine. 374(24). e31–e31. 3 indexed citations
5.
Oliveira, Alexandre G., et al.. (2015). Partial-Hepatectomized (70%) Model Shows a Correlation between Hepatocyte Growth Factor Levels and Beta-Cell Mass. Frontiers in Endocrinology. 6. 20–20. 1 indexed citations
6.
Araújo, Tiago Gomes, et al.. (2013). Liver regeneration following partial hepatectomy is improved by enhancing the HGF/Met axis and Akt and Erk pathways after low-power laser irradiation in rats. Lasers in Medical Science. 28(6). 1511–1517. 22 indexed citations
7.
Araújo, Tiago Gomes, Alexandre G. Oliveira, & Mário J.A. Saad. (2013). Insulin-Resistance-Associated Compensatory Mechanisms of Pancreatic Beta Cells: A Current Opinion. Frontiers in Endocrinology. 4. 146–146. 28 indexed citations
8.
Toque, Haroldo A., Fábio Henrique Silva, Marina C. Calixto, et al.. (2010). High‐fat diet associated with obesity induces impairment of mouse corpus cavernosum responses. British Journal of Urology. 107(10). 1628–1634. 29 indexed citations
9.
Carvalheira, José Barreto Campello, Alexandra B. Ribeiro, Franco Folli, LA Velloso, & Mário J.A. Saad. (2003). Interaction between Leptin and Insulin Signaling Pathways Differentially Affects JAK-STAT and PI 3-Kinase-Mediated Signaling in Rat Liver. Biological Chemistry. 384(1). 151–9. 69 indexed citations
10.
Zecchin, Henrique Gottardello, Rosângela Maria Neves Bezerra, José Barreto Campello Carvalheira, et al.. (2003). Insulin signalling pathways in aorta and muscle from two animal models of insulin resistance—the obese middle-aged and the spontaneously hypertensive rats. Diabetologia. 46(4). 479–491. 52 indexed citations
11.
Carvalho, Carla Roberta de Oliveira, et al.. (2000). Regulation of Cardiac Jak‐2 in Animal Models of Insulin Resistance. IUBMB Life. 49(6). 501–509. 3 indexed citations
12.
Rocha, Eduardo Melani, Carla Roberta de Oliveira Carvalho, Mário J.A. Saad, & Lı́cio A. Velloso. (2000). THE INFLUENCE OF AGING ON TYROSINE KINASE ACTIVITY IN THE INITIAL STEPS OF THE INSULIN SIGNALING SYSTEM IN RAT EXOCRINE GLANDS.. Cornea. 19(Supplement 2). S117–S117. 2 indexed citations
13.
Rocha, Eduardo Melani, et al.. (1999). Insulin-induced tyrosine phosphorylation of Shc in liver, muscle and adipose tissue of insulin resistant rats. Molecular and Cellular Endocrinology. 156(1-2). 121–129. 15 indexed citations
14.
15.
Carvalho, Daniela, et al.. (1998). Angiotensin‐converting enzyme inhibitor increases insulin‐induced pp185 phosphorylation in liver and muscle of obese rats. IUBMB Life. 46(2). 259–266. 3 indexed citations
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18.
Saad, Mário J.A., et al.. (1991). Reduced Cortisol Secretion in Patients with Iron Deficiency. Annals of Nutrition and Metabolism. 35(2). 111–115. 14 indexed citations
19.
Foss, Milton César, et al.. (1990). Peripheral Glucose Metabolism in Human Hyperthyroidism. The Journal of Clinical Endocrinology & Metabolism. 70(4). 1167–1172. 40 indexed citations
20.
Saad, Mário J.A., et al.. (1990). Association of specific histocompatibility antigens and acanthosis nigricans with insulin resistance.. PubMed. 23(10). 959–64. 2 indexed citations

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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