Predictive genomic and transcriptomic analysis on endoscopic ultrasound-guided fine needle aspiration materials from primary pancreatic adenocarcinoma: a prospective multicentre studyResearch in context

Autor: Rémy Nicolle, Cindy Canivet, Laurent Palazzo, Bertrand Napoléon, Mira Ayadi, Camille Pignolet, Jérôme Cros, Sophie Gourgou, Janick Selves, Jérôme Torrisani, Nelson Dusetti, Pierre Cordelier, Louis Buscail, Barbara Bournet, Nicolas Carrère, Fabrice Muscari, Bertrand Suc, Rosine Guimbaud, Corinne Couteau, Marion Deslandres, Pascale Rivera, Emily Alouany, Nadim Fares, Karl Barange, Anne Gomez-Brouchet, Adrian Culetto, Guillaume Le Cosquer, Marion Jaffrelot, Bertrand Pujol, Fabien Fumex, Jérôme Desrame, Christine Lefort, Vincent Lepilliez, Rodica Gincul, Pascal Artru, Léa Clavel, Anne-Isabelle Lemaistre, Sarah Tubiana, Nicolas Flori, Pierre Senesse, Pierre-Emmanuel Colombo, Emmanuelle Samalin-Scalzi, Fabienne Portales, Lise Roca, Claire Honfo Ga, Carinne Plassot, Marc Ychou, Pierre Guibert, Christelle De La Fouchardière, Mathieu Sarabi, Patrice Peyrat, Séverine Tabone-Eglinger, Caroline Renard, Guillaume Piessen, Stéphanie Truant, Alain Saudemont, Guillaume Millet, Florence Renaud, Emmanuelle Leteurtre, Patrick Gelé, Eric Assenat, Jean-Michel Fabre, François-Régis Souche, Marie Dupuy, Anne-Marie Gorce-Dupuy, Jeanne Ramos, Jean-François Seitz, Jean Hardwigsen, Emmanuelle Norguet-Monnereau, Philippe Grandval, Muriel Duluc, Dominique Figarella-Branger, Véronique Vendrely, Clément Subtil, Eric Terrebonne, Jean-Frédéric Blanc, Jean-Philippe Merlio, Dominique Farges-Bancel, Jean-Marc Gornet, Daniela Geromin, Geoffroy Vanbiervliet, Anne-Claire Frin, Delphine Ouvrier, Marie-Christine Saint-Paul, Philippe Berthélémy, Chelbabi Fouad, Stéphane Garcia, Nathalie Lesavre, Mohamed Gasmi, Marc Barthet, Vanessa Cottet, Cyrille Delpierre

Publikováno v: EBioMedicine, Vol 109, Iss , Pp 105373- (2024)

Summary: Background: We apply endoscopic ultrasound-guided fine needle aspiration biopsy to cytopathologically diagnose and sample nucleic acids from primary tumours regardless of the disease stage. Methods: 397 patients with proven pancreatic adenoc

Externí odkaz: https://doaj.org/article/42b1e90f321d4eb681ad93fd73b2d3f5

Fallopian tube lesions as potential precursors of early ovarian cancer: a comprehensive proteomic analysis

Autor: Maxence Wisztorski, Soulaimane Aboulouard, Lucas Roussel, Marie Duhamel, Philippe Saudemont, Tristan Cardon, Fabrice Narducci, Yves-Marie Robin, Anne-Sophie Lemaire, Delphine Bertin, Nawale Hajjaji, Firas Kobeissy, Eric Leblanc, Isabelle Fournier, Michel Salzet

Publikováno v: Cell Death and Disease, Vol 14, Iss 9, Pp 1-20 (2023)

Abstract Ovarian cancer is the leading cause of death from gynecologic cancer worldwide. High-grade serous carcinoma (HGSC) is the most common and deadliest subtype of ovarian cancer. While the origin of ovarian tumors is still debated, it has been s

Externí odkaz: https://doaj.org/article/94827e08cedd45c58c16124d156e2fae

Functional maturation of human iPSC-derived pyramidal neurons in vivo is dependent on proximity with the host tissue

Autor: Célia Raïs, Daniela Gaspar Santos, Giulia Sansone, Stéphane Blanchard, Jean-Pierre Bourgeois, Bernd Jagla, Baptiste Saudemont, Laurène Schlick, Stéphanie Pons, Uwe Maskos

Publikováno v: Frontiers in Cellular Neuroscience, Vol 17 (2023)

Human induced pluripotent stem cells (hiPSCs) have been used extensively in vitro to model early events in neurodevelopment. Because of a number of shortcomings, previous work has established a potential to use these cells in vivo after transplantati

Externí odkaz: https://doaj.org/article/e36c19e1b9ef41d1a6d6d7bc646fb362

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Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification

Autor: Khawla Seddiki, Philippe Saudemont, Frédéric Precioso, Nina Ogrinc, Maxence Wisztorski, Michel Salzet, Isabelle Fournier, Arnaud Droit

Publikováno v: Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)

Convolutional Neural Networks are powerful tools for clinical diagnosis but their effectiveness decreases when the number of available samples is small. Here, the authors develop a cumulative learning method by training the same model through several

Externí odkaz: https://doaj.org/article/61ed017906cc4495a8c8bb0823495394