Renata Sano

2.9k total citations · 1 hit paper
14 papers, 2.4k citations indexed

About

Renata Sano is a scholar working on Cell Biology, Molecular Biology and Physiology. According to data from OpenAlex, Renata Sano has authored 14 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cell Biology, 8 papers in Molecular Biology and 8 papers in Physiology. Recurrent topics in Renata Sano's work include Lysosomal Storage Disorders Research (8 papers), Endoplasmic Reticulum Stress and Disease (5 papers) and Autophagy in Disease and Therapy (4 papers). Renata Sano is often cited by papers focused on Lysosomal Storage Disorders Research (8 papers), Endoplasmic Reticulum Stress and Disease (5 papers) and Autophagy in Disease and Therapy (4 papers). Renata Sano collaborates with scholars based in United States, Brazil and Switzerland. Renata Sano's co-authors include John C. Reed, Alessandra d’Azzo, Alessandra Tessitore, Angela Ingrassia, Ida Annunziata, Michael Forte, Simon Moshiach, Elida Gomero, Joseph T. Opferman and Annette Patterson and has published in prestigious journals such as Journal of Biological Chemistry, Genes & Development and SHILAP Revista de lepidopterología.

In The Last Decade

Renata Sano

14 papers receiving 2.4k citations

Hit Papers

ER stress-induced cell death mechanisms 2013 2026 2017 2021 2013 500 1000 1.5k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. ER stress-induced cell death mechanisms
    2013 1.6k citations Renata Sano, John C. Reed Biochimica et Biophysica Acta (BBA) - Molecular Cell Research profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Renata Sano United States 11 1.2k 1.2k 755 447 194 14 2.4k
Kenji Takehana Japan 17 2.1k 1.7× 726 0.6× 1.6k 2.1× 547 1.2× 270 1.4× 26 3.6k
Hong-Min Ni United States 18 1.5k 1.2× 830 0.7× 1.9k 2.6× 276 0.6× 154 0.8× 22 3.0k
Carol A. Casey United States 31 1.1k 0.9× 818 0.7× 1.3k 1.8× 472 1.1× 199 1.0× 112 3.4k
Damián Gatica United States 13 1.0k 0.8× 664 0.6× 1.1k 1.5× 238 0.5× 146 0.8× 21 2.0k
Stewart Siyan Cao United States 10 944 0.8× 1.0k 0.9× 578 0.8× 204 0.5× 275 1.4× 10 2.1k
Che Xu China 10 1.3k 1.1× 1.1k 0.9× 693 0.9× 216 0.5× 449 2.3× 14 2.7k
Dave Bridges United States 24 1.4k 1.2× 462 0.4× 361 0.5× 385 0.9× 151 0.8× 68 2.3k
Georges Stepien France 18 1.5k 1.2× 539 0.5× 544 0.7× 247 0.6× 129 0.7× 31 2.2k
Gennaro Napolitano United States 19 1.2k 1.0× 524 0.4× 993 1.3× 453 1.0× 338 1.7× 31 2.4k
Karen D. McCullough United States 8 1.3k 1.1× 1.3k 1.1× 678 0.9× 226 0.5× 237 1.2× 11 2.4k

Countries citing papers authored by Renata Sano

Since Specialization
Citations

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

Fields of papers citing papers by Renata Sano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Renata Sano

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

All Works

14 of 14 papers shown
1.
Sano, Renata, Mutiah Apatira, Tarikere Gururaja, et al.. (2023). Bruton's Tyrosine Kinase Inhibitors with Distinct Binding Modes Reveal Differential Functional Impact on B-Cell Receptor Signaling. Molecular Cancer Therapeutics. 23(1). 35–46. 8 indexed citations
2.
Godoi, Paulo H., Asami Hishiki, Renata Sano, et al.. (2016). Orphan Nuclear Receptor NR4A1 Binds a Novel Protein Interaction Site on Anti-apoptotic B Cell Lymphoma Gene 2 Family Proteins. Journal of Biological Chemistry. 291(27). 14072–14084. 16 indexed citations
3.
Sano, Renata & John C. Reed. (2013). ER stress-induced cell death mechanisms. Biochimica et Biophysica Acta (BBA) - Molecular Cell Research. 1833(12). 3460–3470. 1611 indexed citations breakdown →
4.
Benosman, Samir, Palaniyandi Ravanan, Ricardo G. Correa, et al.. (2013). Interleukin-1 Receptor-Associated Kinase-2 (IRAK2) Is a Critical Mediator of Endoplasmic Reticulum (ER) Stress Signaling. PLoS ONE. 8(5). e64256–e64256. 22 indexed citations
5.
Sano, Renata, Michael Hedvat, Ricardo G. Correa, et al.. (2012). Endoplasmic reticulum protein BI-1 regulates Ca2+-mediated bioenergetics to promote autophagy. Genes & Development. 26(10). 1041–1054. 76 indexed citations
6.
Ravanan, Palaniyandi, Renata Sano, Priti Talwar, et al.. (2011). Synthetic Triterpenoid Cyano Enone of Methyl Boswellate Activates Intrinsic, Extrinsic, and Endoplasmic Reticulum Stress Cell Death Pathways in Tumor Cell Lines. Molecular Cancer Therapeutics. 10(9). 1635–1643. 18 indexed citations
7.
Shu, Chih‐Wen, Charitha Madiraju, Dayong Zhai, et al.. (2011). High-Throughput Fluorescence Assay for Small-Molecule Inhibitors of Autophagins/Atg4. SLAS DISCOVERY. 16(2). 174–182. 47 indexed citations
8.
Matte, Úrsula da Silveira, et al.. (2011). Population analysis of the GLB1 gene in South Brazil. SHILAP Revista de lepidopterología. 34(1). 45–48. 17 indexed citations
9.
Sano, Renata, Ida Annunziata, Annette Patterson, et al.. (2009). GM1-Ganglioside Accumulation at the Mitochondria-Associated ER Membranes Links ER Stress to Ca2+-Dependent Mitochondrial Apoptosis. Molecular Cell. 36(3). 500–511. 236 indexed citations
10.
Baldo, Guilherme, et al.. (2008). Transient high-level expression of ß-galactosidase after transfection of fibroblasts from GM1 gangliosidosis patients with plasmid DNA. Brazilian Journal of Medical and Biological Research. 41(4). 283–288. 5 indexed citations
11.
d’Azzo, Alessandra, Alessandra Tessitore, & Renata Sano. (2006). Gangliosides as apoptotic signals in ER stress response. Cell Death and Differentiation. 13(3). 404–414. 72 indexed citations
12.
Sano, Renata, Alessandra Tessitore, Angela Ingrassia, & Alessandra d’Azzo. (2005). Chemokine-induced recruitment of genetically modified bone marrow cells into the CNS of GM1-gangliosidosis mice corrects neuronal pathology. Blood. 106(7). 2259–2268. 68 indexed citations
13.
Sano, Renata, Vera Maria Treis Trindade, Alessandra Tessitore, et al.. (2005). GM1-ganglioside degradation and biosynthesis in human and murine GM1-gangliosidosis. Clinica Chimica Acta. 354(1-2). 131–139. 10 indexed citations
14.
Tessitore, Alessandra, Maria del Pilar Martin, Renata Sano, et al.. (2004). GM1-Ganglioside-Mediated Activation of the Unfolded Protein Response Causes Neuronal Death in a Neurodegenerative Gangliosidosis. Molecular Cell. 15(5). 753–766. 189 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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