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Because many users report continued healing weeks after finishing a cycle, it confirms that the biological repair triggered by the peptide continues after discontinuation

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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

The glucose receptors in brain are a subtype of SLC2 superfamily comprising SGLT-based Na + transporter specific for glucose