Disclosure of interest: Junji Fukumori: nothing to disclose, Kenji Fukutome: nothing to disclose, Shuta Aketa: nothing to disclose, Yasushi Motoyama: nothing to disclose Poster Session I - DIGITAL TRANSFORMATION, AI AND ROBOTICS 07.00 - DIGITAL TRANSFORMATION, AI AND ROBOTICS - 07.01 - TECHNOLOGY INNOVATIONS: ROBOTS, VIRTUAL REALITY, ARTIFICIAL INTELLIGENCE AND MORE P299 - ESOC25-2147 PREDICTING COGNITIVE IMPAIRMENT USING MACHINE LEARNING IN PATIENTS WITH CEREBRAL SMALL VESSEL DISEASES Tingting Mao 1 , Tingting Wang 2 , Yilong Wang 2 , Ling Guan 1,2 1 Beijing Institute of Technology, Beijing, China, 2 Beijing Tiantan Hospital, Beijing, China Background and Aims: Cerebral small vessel disease (CSVD) is a major cause of vascular cognitive impairment (VCI), yet predicting the progression of cognitive decline including different cognitive domains in patients with CSVD still remains challenge
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Fluorescence signals were visualized using a fluorescence microscope (Nikon Ts2R) and quantified by flow cytometry (NovoCyte 3000, Agilent, USA)
Caveolae, a portion of lipid rafts, form through the integration of the protein CAV-1, which are upheld by the cytoskeleton
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