Silvia García

731 total citations
32 papers, 554 citations indexed

About

Silvia García is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Endocrinology, Diabetes and Metabolism. According to data from OpenAlex, Silvia García has authored 32 papers receiving a total of 554 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 13 papers in Cellular and Molecular Neuroscience and 9 papers in Endocrinology, Diabetes and Metabolism. Recurrent topics in Silvia García's work include Neuropeptides and Animal Physiology (11 papers), Receptor Mechanisms and Signaling (8 papers) and Thyroid Disorders and Treatments (6 papers). Silvia García is often cited by papers focused on Neuropeptides and Animal Physiology (11 papers), Receptor Mechanisms and Signaling (8 papers) and Thyroid Disorders and Treatments (6 papers). Silvia García collaborates with scholars based in Argentina, United States and Spain. Silvia García's co-authors include Carlos J. Pirola, Mariano Schuman, Claudio González, Guillermo Dieuzeide, Samuel Finkielman, Ana Marı́a Genaro, Azucena L. Alvarez, Graciela Cremaschi, Alicia Juana Klecha and Marı́a Laura Barreiro Arcos and has published in prestigious journals such as Circulation, The FASEB Journal and Hypertension.

In The Last Decade

Silvia García

31 papers receiving 544 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Silvia García Argentina 15 177 167 112 111 85 32 554
Cai Read United Kingdom 10 169 1.0× 120 0.7× 81 0.7× 253 2.3× 95 1.1× 10 771
Y. Tokuyama Japan 15 131 0.7× 297 1.8× 42 0.4× 83 0.7× 95 1.1× 18 729
Hisashi Koide Japan 14 182 1.0× 170 1.0× 139 1.2× 43 0.4× 90 1.1× 40 633
Kavaljit H. Chhabra United States 14 133 0.8× 155 0.9× 181 1.6× 38 0.3× 124 1.5× 29 592
A. Faletti Argentina 17 91 0.5× 110 0.7× 36 0.3× 91 0.8× 226 2.7× 57 862
Mallikarjuna R. Guruju United States 8 77 0.4× 203 1.2× 130 1.2× 36 0.3× 83 1.0× 10 448
Tatsuo Gonda Japan 14 57 0.3× 211 1.3× 44 0.4× 204 1.8× 130 1.5× 34 693
K. D. Fagin United States 7 256 1.4× 153 0.9× 47 0.4× 31 0.3× 91 1.1× 8 436
Jean‐Pierre Max France 14 80 0.5× 84 0.5× 52 0.5× 131 1.2× 138 1.6× 28 653
Freimut A. Leidenberger Germany 17 269 1.5× 194 1.2× 120 1.1× 56 0.5× 84 1.0× 55 857

Countries citing papers authored by Silvia García

Since Specialization
Citations

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

Fields of papers citing papers by Silvia García

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Silvia García

This figure shows the co-authorship network connecting the top 25 collaborators of Silvia García. A scholar is included among the top collaborators of Silvia García 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 Silvia García. Silvia García 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.
Pirola, Carlos J., et al.. (2024). Metabolic dysfunction-associated steatotic liver disease exhibits sex-specific microbial heterogeneity within intestinal compartments. Clinical and Molecular Hepatology. 31(1). 179–195. 5 indexed citations
2.
3.
Schuman, Mariano, et al.. (2024). Novel Leptin-Cardiac TRH pathway responsible for the cardiac alterations in the Hyperleptinemic obesity. Molecular and Cellular Biochemistry. 480(2). 971–984. 1 indexed citations
4.
García, Silvia, et al.. (2023). Citizen science helps to raise awareness about gut microbiome health in people at risk of developing non-communicable diseases. Gut Microbes. 15(1). 2241207–2241207. 5 indexed citations
5.
6.
Schuman, Mariano, et al.. (2021). Cardiac Thyrotropin-releasing Hormone Inhibition Improves Ventricular Function and Reduces Hypertrophy and Fibrosis After Myocardial Infarction in Rats. Journal of Cardiac Failure. 27(7). 796–807. 4 indexed citations
7.
García, Silvia, et al.. (2020). Cardiovascular and body weight regulation changes in transgenic mice overexpressing thyrotropin-releasing hormone (TRH). Journal of Physiology and Biochemistry. 76(4). 599–608. 8 indexed citations
8.
Schuman, Mariano, et al.. (2018). Angiotensin II requires an intact cardiac thyrotropin-releasing hormone (TRH) system to induce cardiac hypertrophy in mouse. Journal of Molecular and Cellular Cardiology. 124. 1–11. 14 indexed citations
9.
García, Silvia, et al.. (2011). Angiotensin‐(1–7) through Mas receptor up‐regulates neuronal norepinephrine transporter via Akt and Erk1/2‐dependent pathways. Journal of Neurochemistry. 120(1). 46–55. 27 indexed citations
10.
Schuman, Mariano, Jorge E. Toblli, Azucena L. Alvarez, et al.. (2010). Cardiac Thyrotropin-Releasing Hormone Mediates Left Ventricular Hypertrophy in Spontaneously Hypertensive Rats. Hypertension. 57(1). 103–109. 23 indexed citations
11.
Klecha, Alicia Juana, Ana Marı́a Genaro, Gabriela Gorelik, et al.. (2006). Integrative study of hypothalamus–pituitary–thyroid–immune system interaction: thyroid hormone-mediated modulation of lymphocyte activity through the protein kinase C signaling pathway. Journal of Endocrinology. 189(1). 45–55. 105 indexed citations
12.
García, Silvia & Carlos J. Pirola. (2005). Thyrotropin-releasing hormone in cardiovascular pathophysiology. Regulatory Peptides. 128(3). 239–246. 13 indexed citations
13.
Sookoian, Silvia, et al.. (2005). A1166C Angiotensin II Type 1 Receptor Gene Polymorphism May Predict Hemodynamic Response to Losartan in Patients with Cirrhosis and Portal Hypertension. The American Journal of Gastroenterology. 100(3). 636–642. 27 indexed citations
14.
García, Silvia, et al.. (2004). Clinical Features of the Metabolic Syndrome in Adolescents: Minor Role of the Trp64Arg β3-Adrenergic Receptor Gene Variant. Pediatric Research. 55(5). 836–841. 24 indexed citations
15.
García, Silvia, et al.. (2003). Renin–Angiotensin–Aldosterone System Loci and Multilocus Interactions in Young‐Onset Essential Hypertension. Clinical and Experimental Hypertension. 25(2). 117–130. 20 indexed citations
16.
García, Silvia, et al.. (2002). Thyrotropin-Releasing Hormone Decreases Leptin and Mediates the Leptin-Induced Pressor Effect. Hypertension. 39(2). 491–495. 18 indexed citations
17.
Delorenzi, Alejandro, María E. Pedreira, Arturo Romano, et al.. (1996). Angiotensin II enhances long-term memory in the crab Chasmagnathus. Brain Research Bulletin. 41(4). 211–220. 35 indexed citations
18.
García, Silvia, et al.. (1992). Interaction between thyrotrophin-releasing hormone and the muscarinic cholinergic system in rat brain. Journal of Endocrinology. 134(2). 215–219. 15 indexed citations
19.
García, Silvia, et al.. (1992). The cholinergic system participates in thyrotropin-releasing hormone (TRH) regulation. Neuroscience Letters. 135(2). 193–195. 6 indexed citations
20.
Fanelli, Mariel A., et al.. (1992). Brain amines in glucocorticoid-induced hypertension in the rat. Neuroscience Letters. 135(2). 189–192. 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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