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1.发明公开
公开(公告)号：EP4374001A1
公开(公告)日：2024-05-29
申请号：EP22754134.9
申请日：2022-07-21
申请人： Centre National de la Recherche Scientifique , UNIVERSITE DE MONTPELLIER , Universitat Autónoma de Barcelona
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公开(公告)号：EP3911367A1
公开(公告)日：2021-11-24
申请号：EP20726707.1
申请日：2020-01-16
申请人： Universitat Autónoma De Barcelona (UAB) , Fundacio Institut de Recerca de l'Hospital de la Santa Creu i sant Pau , Consorcio Centro de Investigación Biomédica en Red M.P.
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3.发明公开
公开(公告)号：EP4374000A1
公开(公告)日：2024-05-29
申请号：EP22754133.1
申请日：2022-07-21
申请人： Centre National de la Recherche Scientifique , UNIVERSITE DE MONTPELLIER , Universitat Autónoma de Barcelona
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公开(公告)号：EP3427756A1
公开(公告)日：2019-01-16
申请号：EP17382461.6
申请日：2017-07-14
申请人： Universitat Autónoma De Barcelona (UAB) , Fundació Institut de Recerca de l'Hospital de la Santa creu i Sant Pau , Centro en Investigación Biomédica en Red
发明人： VILLAVERDE CORRALES, Antonio , VÁZQUEZ GÓMEZ, Esther , UNZUETA ELORZA, Ugutz , MANGUES BAFALLUY, Ramon , CÉSPEDES NAVARRO, María Virtudes , CASANOVA RIGAT, Isolda
摘要： The present invention relates to nanostructured conjugates, more specifically to nanostructured fusion proteins suitable for the selective delivery of their conjugated therapeutic agents to specific cell and tissue types. It also relates to nanoparticles comprising such nanostructured proteins and the therapeutic uses thereof.
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5.发明授权HAPTENES AND CONJUGATES DERIVED FROM PYOCYANIN, ANTIBODIES THEREOF, AND IMMUNOCHEMICAL METHOD FOR DETECTING INFECTIONS CAUSED BY PSEUDOMONAS AERUGINOSA 有权衍生自粘菌素的HAPTENES和缀合物，其抗体以及用于检测由铜绿假单胞菌引起的感染的免疫化学方法
公开(公告)号：EP2966066B1
公开(公告)日：2018-02-14
申请号：EP14760605.7
申请日：2014-03-04
申请人： Consejo Superior De Investigaciones Científicas (CSIC) , Consorcio Centro de Investigación Biomédica en Red M.P. , Universitat Autónoma De Barcelona (UAB)
发明人： MARCO COLÁS, María Pilar , PASCUAL DURÁN, Nuria , PASTELLS DÍEZ, Carme , SANCHEZ BAEZA, Francisco , VILLAVERDE CORRALES, Antonio Pedro , RODRÍGUEZ CARMONA, Escarlata
IPC分类号： C07D241/46 , C07K16/12 , G01N33/569
CPC分类号： G01N33/5308 , C07D241/46 , C07K16/44 , C07K19/00 , G01N33/56911 , G01N2333/21 , G01N2469/10
摘要： The invention relates to a compound of general formula (I) and to the use thereof as a haptene. The invention also relates to a conjugate of said compound I with a carrier protein or fragment thereof, with a detectable marking agent, or with a polymer or support, and to the use thereof for producing antibodies. The invention also relates to a method for the detection and/or quantification of 1-hydroxyphenazine and/or pyocyanin using said antibodies and conjugates for the detection of infections caused by Pseudomonas aeruginosa.
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公开(公告)号：EP4128250A1
公开(公告)日：2023-02-08
申请号：EP20712816.6
申请日：2020-03-30
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公开(公告)号：EP3446075B1
公开(公告)日：2020-04-01
申请号：EP16717937.3
申请日：2016-04-21
申请人： Consejo Superior de Investigaciones Científicas (CSIC) , Universitat Autónoma De Barcelona (UAB) , Kromschroeder, S.A.
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8.发明公开
公开(公告)号：EP4398132A1
公开(公告)日：2024-07-10
申请号：EP23382001.8
申请日：2023-01-03
申请人： Centre De Visió Per Computador , Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol (IGTP) , Universitat Autónoma de Barcelona
发明人： GIL RESINA, Debora , SANCHEZ RAMOS, Carles , ROSELL GRATACÓS, Antoni , BAEZA MENA, Sonia Margarita , TORRES, Guillermo Eduardo
IPC分类号： G06F18/2413 , A61B5/00 , G06T7/00 , G16H50/20
CPC分类号： G06V2201/03120220101 , G06F18/24133 , G06T7/0012 , G16H50/20 , G16H30/40 , G06V10/82
摘要： A method, system and computer programs for characterization of pathologies are provided. The method comprises accessing or receiving image data and clinical and demographic data and/or genomic information, the image data including different types of scans of a subject, and the clinical and demographic data including attributes of a group of subjects; computing radiomic features using a feature extraction algorithm and embedding the computed radiomic features and the clinical and demographic data and/or genomic information in an abstract space representing the lesion; performing a characterization of the lesion at different levels by determining categorical and quantitative variables associated to each level using the computed radiomic features, providing a multi-diagnostic and/or -prognostic characterization of the lesion as a result; and transforming the provided multi-diagnostic and/or -prognostic characterization of the lesion into radiological words or annotations that quantitatively and/or qualitatively describe the lesion using deep learning or machine learning algorithms.
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