Post-Operative Outcome Predictions in Vestibular Schwannoma Using Machine Learning Algorithms.

This study aimed to develop a machine learning (ML) algorithm that can predict unplanned reoperations and surgical/medical complications after vestibular schwannoma (VS) surgery. All pre- and peri-operative variables available in the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database (n = 110), except those directly related to our outcome variables, were used as input variables. A deep neural network model consisting of seven layers was developed using the Keras open-source library, with a 70:30 breakdown for training and testing.

AAV-based delivery of RNAi targeting Ataxin-2 improves survival, strength, and pathology in mouse models of rapidly and slowly progressive sporadic ALS.

Amyotrophic lateral sclerosis (ALS) is characterized by motor neuron death due to nuclear loss and cytoplasmic aggregation of the splice factor TDP-43. Pathologic TDP-43 associates with stress granules (SGs) and downregulating the SG-associated protein Ataxin-2 (Atxn2) using antisense oligonucleotides (ASO) prolongs survival in the TAR4/4 sporadic ALS mouse model, a strategy now in clinical trials. Here, we used AAV-mediated RNAi delivery to achieve lasting and targeted knockdown after a single injection.