Laure Jamot

1.6k total citations · 1 hit paper
17 papers, 1.3k citations indexed

About

Laure Jamot is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Surgery. According to data from OpenAlex, Laure Jamot has authored 17 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Cellular and Molecular Neuroscience, 8 papers in Molecular Biology and 4 papers in Surgery. Recurrent topics in Laure Jamot's work include Mitochondrial Function and Pathology (5 papers), Genetic Neurodegenerative Diseases (4 papers) and Neuropeptides and Animal Physiology (3 papers). Laure Jamot is often cited by papers focused on Mitochondrial Function and Pathology (5 papers), Genetic Neurodegenerative Diseases (4 papers) and Neuropeptides and Animal Physiology (3 papers). Laure Jamot collaborates with scholars based in France, United States and Canada. Laure Jamot's co-authors include John Roder, Xiao‐Jiang Li, Erica Rosemond, Krista McCutcheon, N. Agopyan, Michael R. Hayden, Steven M. Hersch, John Hodgson, Mary E. Stevens and Edward M. Rubin and has published in prestigious journals such as Neuron, Journal of Neuroscience and Hepatology.

In The Last Decade

Laure Jamot

17 papers receiving 1.2k citations

Hit Papers

A YAC Mouse Model for Huntington’s Disease with Full-Leng... 1999 2026 2008 2017 1999 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Laure Jamot France 11 946 908 333 84 83 17 1.3k
Susanne C. Bleckmann Germany 8 423 0.4× 581 0.6× 52 0.2× 48 0.6× 139 1.7× 8 955
Caroline Rouaux France 17 516 0.5× 935 1.0× 353 1.1× 35 0.4× 184 2.2× 23 1.5k
Raphaël Hourez Belgium 12 547 0.6× 755 0.8× 211 0.6× 20 0.2× 81 1.0× 15 1.2k
R Oppenheim United States 16 569 0.6× 536 0.6× 75 0.2× 31 0.4× 53 0.6× 17 1.0k
Tennore Ramesh United Kingdom 15 125 0.1× 405 0.4× 287 0.9× 52 0.6× 42 0.5× 25 936
Sara Duarte‐Silva Portugal 18 489 0.5× 502 0.6× 200 0.6× 15 0.2× 61 0.7× 38 811
Yanmei Tao China 14 466 0.5× 733 0.8× 66 0.2× 29 0.3× 86 1.0× 20 1.2k
Nayana Lahiri United Kingdom 15 575 0.6× 610 0.7× 206 0.6× 27 0.3× 57 0.7× 27 1.0k
Valérie Coronas France 18 388 0.4× 357 0.4× 41 0.1× 25 0.3× 36 0.4× 31 874
Patrik Brundin Sweden 13 1.2k 1.3× 902 1.0× 617 1.9× 100 1.2× 62 0.7× 19 1.7k

Countries citing papers authored by Laure Jamot

Since Specialization
Citations

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

Fields of papers citing papers by Laure Jamot

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laure Jamot

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

All Works

17 of 17 papers shown
1.
Philippe, Erwann, Florence Mehl, Jessica Denom, et al.. (2022). Regenerating islet-derived protein 3α: A promising therapy for diabetes. Preliminary data in rodents and in humans. Heliyon. 8(7). e09944–e09944. 3 indexed citations
2.
Cruciani‐Guglielmacci, Céline, Patrick Gonzalez, Paul Amouyal, et al.. (2020). 1064-P: The Human Recombinant REG3A Protein ALF-5755 Restores Glucose Homeostasis and Insulin Sensitivity in High-Fat Fed Mice and in Ob/Ob Mice. Diabetes. 69(Supplement_1). 1 indexed citations
3.
Calvas, Patrick, et al.. (2017). The RaDiCo ACOEIL : a French rare disease cohort dedicated to ocular developmental anomalies in children. Acta Ophthalmologica. 95(S259). 1 indexed citations
4.
Vibert, Éric, Gabriella Pittau, Maximiliano Gelli, et al.. (2013). Actual incidence and long-term consequences of posthepatectomy liver failure after hepatectomy for colorectal liver metastases. Surgery. 155(1). 94–105. 44 indexed citations
5.
Moniaux, Nicolas, Alexandre Dos Santos, Laure Jamot, et al.. (2012). HIP/PAP, un nouveau médicament contre les hépatites aiguës graves. médecine/sciences. 28(3). 239–241. 2 indexed citations
6.
Moniaux, Nicolas, Haiyan Song, Kévin Garbin, et al.. (2010). Human hepatocarcinoma-intestine-pancreas/pancreatitis-associated protein cures fas-induced acute liver failure in mice by attenuating free-radical damage in injured livers. Hepatology. 53(2). 618–627. 32 indexed citations
7.
Valenza, Marta, Dorotea Rigamonti, Donato Goffredo, et al.. (2005). Dysfunction of the Cholesterol Biosynthetic Pathway in Huntington's Disease. Journal of Neuroscience. 25(43). 9932–9939. 218 indexed citations
8.
Sluyter, Frans, et al.. (2005). Prenatal exposure to alcohol does not affect radial maze learning and hippocampal mossy fiber sizes in three inbred strains of mouse. Behavioral and Brain Functions. 1(1). 5–5. 12 indexed citations
9.
Jamot, Laure, Hans W. D. Matthes, Frédéric Simonin, Brigitte L. Kieffer, & John Roder. (2003). Differential involvement of the Mu and Kappa opioid receptors in spatial learning. Genes Brain & Behavior. 2(2). 80–92. 64 indexed citations
10.
Takeuchi, Junichi, Zhengping Jia, Laure Jamot, et al.. (2002). Increased drinking in mutant mice with truncated M5 muscarinic receptor genes. Pharmacology Biochemistry and Behavior. 72(1-2). 117–123. 28 indexed citations
11.
Fujigasaki, Hiroto, Ishwar C. Verma, A. Camuzat, et al.. (2001). SCA12 is a rare locus for autosomal dominant cerebellar ataxia: A study of an Indian family. Annals of Neurology. 49(1). 117–121. 72 indexed citations
12.
Fujigasaki, Hiroto, I. C. Verma, Agnès Camuzat, et al.. (2001). SCA12 is a rare locus for autosomal dominant cerebellar ataxia: A study of an Indian family. Annals of Neurology. 49(1). 117–121. 6 indexed citations
13.
Hodgson, John, N. Agopyan, Claire‐Anne Gutekunst, et al.. (1999). A YAC Mouse Model for Huntington’s Disease with Full-Length Mutant Huntingtin, Cytoplasmic Toxicity, and Selective Striatal Neurodegeneration. Neuron. 23(1). 181–192. 669 indexed citations breakdown →
14.
Yeomans, John, Junko S. Takeuchi, Zhengping Jia, et al.. (1999). Gene-targetted mice lacking M5 receptors: Genotypes and behavior. Life Sciences. 64(6-7). 588–588. 6 indexed citations
15.
Roy, Isabelle Le, Pierre L. Roubertoux, Laure Jamot, et al.. (1998). Neuronal and behavioral differences between Mus musculus domesticus (C57BL/6JBy) and Mus musculus castaneus (CAST/Ei). Behavioural Brain Research. 95(1). 135–142. 36 indexed citations
16.
Jamot, Laure, et al.. (1994). Neuroanatomical divergence between two substrains of C57BL/6J inbred mice entails differential radial-maze learning. Brain Research. 644(2). 352–356. 24 indexed citations
17.
Sluyter, Frans, Laure Jamot, Geert A. van Oortmerssen, & Wim E. Crusio. (1994). Hippocampal mossy fiber distributions in mice selected for aggression. Brain Research. 646(1). 145–148. 45 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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