Laëtitia Guével

488 total citations
18 papers, 344 citations indexed

About

Laëtitia Guével is a scholar working on Molecular Biology, Genetics and Surgery. According to data from OpenAlex, Laëtitia Guével has authored 18 papers receiving a total of 344 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 6 papers in Genetics and 5 papers in Surgery. Recurrent topics in Laëtitia Guével's work include Muscle Physiology and Disorders (7 papers), Mesenchymal stem cell research (6 papers) and Tissue Engineering and Regenerative Medicine (5 papers). Laëtitia Guével is often cited by papers focused on Muscle Physiology and Disorders (7 papers), Mesenchymal stem cell research (6 papers) and Tissue Engineering and Regenerative Medicine (5 papers). Laëtitia Guével collaborates with scholars based in France, Canada and Morocco. Laëtitia Guével's co-authors include Karl Rouger, Laurence Dubreil, Vehary Sakanyan, Lynn A. Megeney, Marina Snapyan, Michèle Lecocq, Anahit Ghochikyan, Mireille Ledevin, Gwennan André‐Grégoire and Julie Gavard and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Cancer Research.

In The Last Decade

Laëtitia Guével

17 papers receiving 333 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Laëtitia Guével France 11 277 66 65 49 40 18 344
Teresa Nieto-Miguel Spain 16 266 1.0× 65 1.0× 93 1.4× 71 1.4× 24 0.6× 22 782
Frida Holm Sweden 11 430 1.6× 80 1.2× 48 0.7× 25 0.5× 41 1.0× 21 505
Methichit Wattanapanitch Thailand 12 283 1.0× 57 0.9× 69 1.1× 20 0.4× 28 0.7× 35 400
Yongli Shan China 10 524 1.9× 114 1.7× 53 0.8× 32 0.7× 38 0.9× 27 625
Yuzuru Sasamoto United States 12 236 0.9× 47 0.7× 25 0.4× 40 0.8× 21 0.5× 26 722
Mario Mairhofer Austria 13 227 0.8× 64 1.0× 25 0.4× 109 2.2× 81 2.0× 23 494
Kumaran Chandrasekharan United States 14 286 1.0× 51 0.8× 26 0.4× 39 0.8× 59 1.5× 17 488
В. В. Терских Russia 11 193 0.7× 33 0.5× 34 0.5× 87 1.8× 43 1.1× 39 427
Nicolas Dard France 13 358 1.3× 51 0.8× 100 1.5× 135 2.8× 25 0.6× 15 589
Sara Lööf Sweden 7 286 1.0× 83 1.3× 18 0.3× 49 1.0× 26 0.7× 10 378

Countries citing papers authored by Laëtitia Guével

Since Specialization
Citations

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

Fields of papers citing papers by Laëtitia Guével

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laëtitia Guével

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

All Works

18 of 18 papers shown
1.
Richard, Mathilde, Richard Moreau, Mikaël Croyal, et al.. (2024). Monitoring concentration and lipid signature of plasma extracellular vesicles from HR+ metastatic breast cancer patients under CDK4/6 inhibitors treatment. SHILAP Revista de lepidopterología. 3(12). e70013–e70013.
2.
Richard, Mathilde, Mario Campone, Marie Robert, et al.. (2023). Abstract P3-11-09: Extracellular vesicle-based biomarker assay for the monitoring of the efficacy of frontline endocrine therapy + CDK4-6 inhibitors in metastatic breast cancer. Cancer Research. 83(5_Supplement). P3–11. 1 indexed citations
3.
André‐Grégoire, Gwennan, Carolina Alves Nicolau, Aurélien Dupont, et al.. (2021). The von Willebrand factor stamps plasmatic extracellular vesicles from glioblastoma patients. Scientific Reports. 11(1). 22792–22792. 24 indexed citations
4.
André‐Grégoire, Gwennan, et al.. (2020). Vesiclemia: counting on extracellular vesicles for glioblastoma patients. Oncogene. 39(38). 6043–6052. 18 indexed citations
5.
Toumaniantz, Gilles, Thibaut Larcher, Isabelle Leroux, et al.. (2020). Human MuStem Cell Grafting into Infarcted Rat Heart Attenuates Adverse Tissue Remodeling and Preserves Cardiac Function. Molecular Therapy — Methods & Clinical Development. 18. 446–463. 6 indexed citations
6.
Lardenois, Aurélie, Isabelle Leroux, Blandine Lieubeau, et al.. (2018). Human serum and platelet lysate are appropriate xeno-free alternatives for clinical-grade production of human MuStem cell batches. Stem Cell Research & Therapy. 9(1). 128–128. 33 indexed citations
7.
Babarit, Candice, Thibaut Larcher, Laurence Dubreil, et al.. (2016). Identification in GRMD dog muscle of critical miRNAs involved in pathophysiology and effects associated with MuStem cell transplantation. BMC Musculoskeletal Disorders. 17(1). 209–209. 8 indexed citations
8.
Lardenois, Aurélie, Mélanie Lagarrigue, Blandine Guével, et al.. (2016). Quantitative proteome profiling of dystrophic dog skeletal muscle reveals a stabilized muscular architecture and protection against oxidative stress after systemic delivery of MuStem cells. PROTEOMICS. 16(14). 2028–2042. 18 indexed citations
9.
Lardenois, Aurélie, Candice Babarit, Thibaut Larcher, et al.. (2015). Differential Gene Expression Profiling of Dystrophic Dog Muscle after MuStem Cell Transplantation. PLoS ONE. 10(5). e0123336–e0123336. 16 indexed citations
10.
Scaglia, H.E., et al.. (2012). Quantification of Testosterone (T) by 8 Immunoassays and by Liquid Chromatography -Tandem Mass Spectrometry (LC-MSMS) in Normal and Hirsute Women: A Multicenter Study. 49(4). 0–0. 1 indexed citations
11.
Guével, Laëtitia, Jessie R. Lavoie, Carolina Perez‐Iratxeta, et al.. (2011). Quantitative Proteomic Analysis of Dystrophic Dog Muscle. Journal of Proteome Research. 10(5). 2465–2478. 64 indexed citations
12.
Guével, Laëtitia, Karl Rouger, Laurence Dubreil, et al.. (2009). PTEN Contributes to Profound PI3K/Akt Signaling Pathway Deregulation in Dystrophin-Deficient Dog Muscle. American Journal Of Pathology. 174(4). 1459–1470. 36 indexed citations
13.
Rouaud, Thierry, Aude Lafoux, Dmitri O. Levitsky, et al.. (2007). Fetal muscle-derived cells can repair dystrophic muscles in mdx mice. Experimental Cell Research. 313(5). 997–1007. 10 indexed citations
14.
Guével, Laëtitia, et al.. (2007). Progenitor Cell Isolation from Muscle-derived Cells based on Adhesion Properties. Journal of Histochemistry & Cytochemistry. 55(6). 607–618. 30 indexed citations
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17.
Ghochikyan, Anahit, Michèle Lecocq, Patricia Vusio, et al.. (2002). Arginine Operator Binding by Heterologous and Chimeric ArgR Repressors fromEscherichia coliandBacillus stearothermophilus. Journal of Bacteriology. 184(23). 6602–6614. 21 indexed citations
18.
Giardina, Thierry, et al.. (1999). Cloning, sequencing and further characterization of acylpeptide hydrolase from porcine intestinal mucosa. Biochimica et Biophysica Acta (BBA) - Protein Structure and Molecular Enzymology. 1432(2). 371–381. 22 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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