Purpose: To describe our process for returning genetic results to participants in the Colorado Center for Personalized Medicine biobank.
Methods: Enrollment in the biobank is open to all adult UCHealth patients. Participants who provided a sample that was genotyped and signed the proper consent were eligible to receive results.
Background: Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable factors that can identify and predict CMD risk and be targeted through interventions. This systematic review aimed to synthesise evidence from ML studies predicting CMDs, evaluate their performance, and establish the potential benefit of incorporating lifestyle data in ML models alongside biological and/or demographic-environmental factors.
Methods: This systematic review adheres to the PRISMA statement (Prospero CRD42023401194).
Background: The prenatal and early-life periods pose a crucial neurodevelopmental window whereby disruptions to the intestinal microbiota and the developing brain may have adverse impacts. As antibiotics affect the human intestinal microbiome, it follows that early-life antibiotic exposure may be associated with later-life psychiatric or neurocognitive outcomes.
Aims: To explore the association between early-life (in utero and early childhood (age 0-2 years)) antibiotic exposure and the subsequent risk of psychiatric and neurocognitive outcomes.
Introduction/aims: Hereditary transthyretin amyloidosis (ATTRv) is a genetic condition caused by pathogenic variants in the transthyretin (TTR) gene resulting in multisystem amyloid deposition, especially in peripheral nerve and heart. Information on the prevalence of ATTRv in the United States is limited. The objective of this study was to understand the prevalence and genetic ancestry in the Val142Ile population in a large regional US population.
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