Background: Investigators conducting clinical trials have an ethical, scientific, and regulatory obligation to protect the safety of trial participants. Traditionally, safety monitoring includes manual review and coding of adverse event data by expert clinicians.
Objectives: Our study explores the use of natural language processing (NLP) and artificial intelligence (AI) methods to streamline and standardize clinician coding of adverse event data in Alzheimer's disease (AD) clinical trials.
Introduction: Down Syndrome Regression Disorder (DSRD) is a neuropsychiatric condition causing insomnia, catatonia, encephalopathy, and obsessive-compulsive behavior in otherwise healthy individuals with Down syndrome (DS). Smaller cohorts have identified heterogenous diagnostic abnormalities which have predicted immunotherapy responsiveness although pattern analysis in a large cohort has never been performed.
Methods: A multi-center, retrospective study of individuals with DSRD was performed.
The U1 snRNP complex recognizes pre-mRNA splicing sites in the early stages of spliceosome assembly and suppresses premature cleavage and polyadenylation. Its dysfunction may precede Alzheimer's disease (AD) hallmarks. Here we evaluated the effects of a synthetic single-stranded cDNA (APT20TTMG) that interacts with U1 snRNP, in iPSC-derived neurons from a donor diagnosed with AD and in the SAMP8 mouse model.
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