Showing 161 - 164 results of 164 for search 'International Mass Spectrometry Foundation', query time: 0.11s Refine Results
  1. 161
    Advanced Quantitative Lipoprotein Characteristics Do Not Relate to Healthy Dietary Patterns in Adults from a Mediterranean Area

    Advanced Quantitative Lipoprotein Characteristics Do Not Relate to Healthy Dietary Patterns in Adults from a Mediterranean Area by Marina Idalia Rojo-López, Esmeralda Castelblanco, Jordi Real, Marta Hernández, Mireia Falguera, Núria Amigó, Josep Julve, Núria Alonso, Josep Franch-Nadal, Minerva Granado-Casas, Dídac Mauricio

    Published 2021-12-01
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  2. 162
    Extracellular vesicles from thyroid cancer harbor a functional machinery involved in extracellular matrix remodeling

    Extracellular vesicles from thyroid cancer harbor a functional machinery involved in extracellular matrix remodeling by Rocío del Carmen Bravo-Miana, María Florencia Soler, Danilo Guillermo Ceschin, Félix Royo, Dana María Negretti-Borga, Mikel Azkargorta, Félix Elortza, María del Mar Montesinos, Claudia Gabriela Pellizas, Juan Manuel Falcón-Pérez, Ana Carolina Donadio

    Published 2022-06-01
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  3. 163
    Molecular Identification and Toxin Analysis of <i>Alexandrium</i> spp. in the Beibu Gulf: First Report of Toxic <i>A. tamiyavanichii</i> in Chinese Coastal Waters

    Molecular Identification and Toxin Analysis of <i>Alexandrium</i> spp. in the Beibu Gulf: First Report of Toxic <i>A. tamiyavanichii</i> in Chinese Coastal Waters by Yixiao Xu, Xilin He, Huiling Li, Teng Zhang, Fu Lei, Haifeng Gu, Donald M. Anderson

    Published 2021-02-01
    “…To explore the nature of the threat from PSTs in the region, eight <i>Alexandrium</i> strains were isolated from waters of the Beibu Gulf and examined using phylogenetic analyses of large subunit (LSU) rDNA, small subunit (SSU) rDNA, and internal transcribed spacer (ITS) sequences. Their toxin composition profiles were also determined using liquid chromatography-tandem mass spectrometry (LC-MS/MS). …”
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  4. 164
    Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study

    Machine learning for classification of hypertension subtypes using multi-omics: A multi-centre, retrospective, data-driven study by Parminder S. Reel, Smarti Reel, Josie C. van Kralingen, Katharina Langton, Katharina Lang, Zoran Erlic, Casper K. Larsen, Laurence Amar, Christina Pamporaki, Paolo Mulatero, Anne Blanchard, Marek Kabat, Stacy Robertson, Scott M. MacKenzie, Angela E. Taylor, Mirko Peitzsch, Filippo Ceccato, Carla Scaroni, Martin Reincke, Matthias Kroiss, Michael C. Dennedy, Alessio Pecori, Silvia Monticone, Jaap Deinum, Gian Paolo Rossi, Livia Lenzini, John D. McClure, Thomas Nind, Alexandra Riddell, Anthony Stell, Christian Cole, Isabella Sudano, Cornelia Prehn, Jerzy Adamski, Anne-Paule Gimenez-Roqueplo, Guillaume Assié, Wiebke Arlt, Felix Beuschlein, Graeme Eisenhofer, Eleanor Davies, Maria-Christina Zennaro, Emily Jefferson

    Published 2022-10-01
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