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Windisch, M., Gschanes, A
Disclosures: xiangnan teng: Nothing to Disclose, Jin Shang: Nothing to Disclose, Lingyao Du: Nothing to Disclose, hong Tang: Nothing to Disclose, Lang Bai: Nothing to Disclose 1826 A CLINICOPATHOLOGICAL AND ARTIFICIAL INTELLIGENCE-DERIVED SCREENING MODEL AS A TOOL FOR DISTINGUISHING PATIENTS WITH NON-HODGKIN LYMPHOMAS FROM INFLAMMATORY HEPATIC DISEASES: A MULTICENTRIC LEARNING APPROACH Mina Farag 1 Andre Lametti 1 Ajay Rajaram 1 Nadine Demko 1 Pari Jafari 2 Ruoji Zhou 3 Alison Xiaotang Du 3 Pierre Fiset 1 Amine Benmassaoud 4 Farhad Maleki 5 Victoria Marcus 1 Rene P MICHEL 1 Amy Chadburn 6 John Hart 2 Greta Evaristo 7 , 1 McGill University, 2 University of Chicago, 3 UCLA Medical Center, 4 McGill Univertsity, 5 University of Calgary, 6 Weill Cornell Medicine, 7 McGill Univeristy Background: Primary hepatic non-Hodgkin lymphomas (NHL) are rare, with nonspecific and often subtle clinical presentations

Pushing through excessive fatigue during aggressive fat loss protocols leads to overtraining syndrome, which actually increases muscle catabolism and slows fat loss
Therefore, glutamate accumulates in the extracellular space, resulting in a large increase in the excitatory amino acid concentration (Wang et al., 2007)