Disclosure of interest: NO 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 P308 - ESOC25-442 CLINICIAN PERSPECTIVES ON MACHINE LEARNING TOOLS FOR OUTCOME PREDICTION AND DECISION-MAKING IN INTRACEREBRAL HAEMORRHAGE Alexandra Hurden 1 , Menglu Ouyang, Leibo Liu 1,2 , Xiaoying Chen 1 , Craig Anderson 1 The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, Australia, 2 Centre for Big Data Research in Health, Faculty of Medicine, University of New South Wales, Sydney, Australia Background and Aims: Machine learning (ML) tools hold promise in outcome prediction and assisting clinicians decision making for patients with acute intracerebral haemorrhage (ICH)
Ketamine alone and combined with diazepam or xylazine in laboratory animals: a 10 year experience
264 Consequently, microRNA antagonists may offer potential strategies to counteract these deleterious effects
View Abstract ESCGT 2008: progress in clinical gene therapy
BACH1 and Tumor Metastasis BACH1 facilitates the metastasis of several cancers, including breast cancer, [ 75 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 ] lung cancer, [ 31 , 39 , 230 ] pancreatic cancer, [ 231 , 232 , 233 ] colorectal cancer, [ 46 , 234 , 235 , 236 , 237 , 238 ] ovarian cancer, [ 239 , 240 , 241 ] glioma, [ 242 , 243 ] and renal cell carcinoma
