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Disclosure of interest: All of authors: nothing to disclose 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 P895 - ESOC25-2267 COMPARING MACHINE LEARNING MODELS FOR DIFFERENTIATING STROKE SUBTYPES USING BIOMARKERS AND IMAGING MODALITIES Manabesh Nath 1 , Pradeep Kumar 2 , Shubham Misra 3 , Manjari Tripathi 1 , Sachin Kumar 4 , Shantanu Sengupta 5 , Awadh Pandit 1 , Deepti Vibha 1 1 Department of Neurology All India Institute of Medical Sciences, Neurosciences Centre, New Delhi, India, 2 Clinical Research Unit, All India Institute of Medical Sciences, New Delhi, India, 3 Department of Neurology, Yale University, New Haven, United States, 4 Center for Biomedical Engineering, Indian Institute of Technology, New Delhi, India, 5 Institute of Genomics and Integrative Biology, New Delhi, India Background and Aims: Accurate and rapid stroke subtype differentiation is crucial for effective treatment
Cryo-EM data were acquired on a 300 kV Titan Krios electron microscope (Thermo Fisher Scientific) equipped with a K3 Summit direct electron detector (Gatan) mounted after a quantum energy filter set to a slit width of 20 eV
Nitric Oxide Modulation: The interaction with the NO system to protect endothelial tissue from oxidative stress and toxin-induced damage
Its broad anti-inflammatory effects and multiple administration routes offer versatility in targeting different areas of the body in research models, promising further investigation into inflammatory disorder mechanisms
