2023 International Congress

August 27-31, 2023. Copenhagen, Denmark.

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LRRK2 kinase activity regulates sphingolipid hydrolase enzymatic activities in different cell lines: induced pluripotent stem cell-derived dopaminergic neurons, primary monocyte‐derived macrophages and SH-SY5Y

T. Usenko, K. Basharova, A. Bezrukova, A. Izyumchenko, M. Nikolaev, G. Baydakova, S. Pavlova, E. Grigorieva, S. Medvedev, E. Zakharova, S. Zakian, S. Pchelina (Gatchina, Russian Federation)
LRRK2 PROTAC® degrader molecules induce robust biomarker responses in preclinical in vivo pharmacology studies following acute and chronic oral dosing

J. Meredith Jr, K. Kelly, V. Guss, A. Desantis, L. Kimmel, S. Soletsky, J. Pizzano, G. Cadelina, J. Chavez, S. Sparks, A. Cacace (New Haven, USA)
Lysosomal-driven reduction of alpha-synuclein aggregates in a neuron-like model of Parkinson’s disease

S. Lucas-Del-Pozo, G. Uras, F. Fierli, AHV. Schapira (London, United Kingdom)
Machine learning classifies Parkinson’s Disease and Progressive Supranuclear Palsy on saccade, pupil, and blink measures during a naturalistic free-viewing task

D. Brien, H. Riek, R. Yep, J. Huang, B. Coe, B. White, M. Habibi, D. Grimes, M. Jog, A. Lang, C. Marras, M. Masellis, P. Mclaughlin, A. Peltsch, A. Roberts, B. Tan, D. Beaton, W. Lou, E. Finger, A. Frank, D. Tang-Wai, C. Tartaglia, S. Black, R. Swartz, W. Oertel, D. Munoz (Kingston, Canada)
Machine learning diagnosis of parkinsonian syndromes: network approach with two different sites

M. Perovnik, T. Rus, A. Vo, N. Nguyen, P. Tomše, J. Jamšek, C. Tang, M. Trošt, D. Eidelberg (Ljubljana, Slovenia)
Machine Learning Model for Prediction of acute orthostatic hypotension after levodopa administration

Z. Liu, SN. Lin, ZL. Chen, Y. Ling, T. Feng (Beijing, China)
Machine Learning Prediction of Gait Dysfunction from DTI Multi-Spectral Diffusion Weighted Imaging ROIs

C. Raimondo, I. Ailes, I. Shelley, K. Shivok, M. Syed, M. Alizadeh (Philadelphia, USA)
Machine Learning Prediction of Motor Score Reduction following DBS Surgery in PD

C. Raimondo, C. Wu, M. Alizadeh (Philadelphia, USA)
Machine learning reveals candidate genes from Parkinson’s disease associated loci

E. Yu, R. Lariviere, R. Thomas, L. Liu, K. Senkevich, E. Fon, Z. Gan-Or (Montreal, Canada)
Machine learning selects common fMRI features of cognitive impairment in Parkinson’s disease from cross-institution datasets

CY. Chien, TY. Lin, TL. Lee, CCK. Lin (Tainan, Taiwan)

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