Poster Session II - DIGITAL TRANSFORMATION, AI AND ROBOTICS 07.00 - DIGITAL TRANSFORMATION, AI AND ROBOTICS - 07.01 - TECHNOLOGY INNOVATIONS: ROBOTS, VIRTUAL REALITY, ARTIFICIAL INTELLIGENCE AND MORE P901 - ESOC25-883 EXPLAINABLE MACHINE LEARNING-BASED PREDICTION OF GOOD FUNCTIONAL OUTCOME AFTER THROMBECTOMY FOR LARGE AND MEDIUM VESSEL OCCLUSION ISCHEMIC STROKE Michele Romoli 1 , Federica Sepe 2 , Francesco Cordici 1 , Sara Biguzzi 1 , Sebastiano Giacomozzi 1 , Elvis Lafe 1 , Delfina Ferrandi 3 , Ivan Gallesio 3 , Marco Longoni 1 , Federico Menetti 1 , Maria Ruggiero 1 , Giacomo Lancia 4 1 Department of Neurosciences, Bufalini Hospital, Cesena, Italy, Cesena, Italy, 2 IRCCS Mondino, Pavia, Italy, 3 Alessandria Hospital, Alessandria, Italy, 4 Universit La Sapienza, Roma, Italy Background and Aims: Despite largely used, ASPECT score has an insufficient accuracy in predicting the functional recovery after acute ischemic stroke and large inter-rater reliability
This work provides an elegant example of mechanochemical coupling in integrins, showing that signalling events involving plexin D1 and semaphorin 3E regulate the mechanical properties (catch bond behaviour) of integrins
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