Serotonergic psychedelic 5-MeO-DMT alters plasticity-related gene expression and generates anxiolytic effects in stressed mice.

Serotonergic psychedelics have potential therapeutic effects in treating anxiety and mood disorders, often after a single dose, and are suggested to have plasticity-inducing action. However, a comprehensive mechanism of action is still lacking. Here, we investigated how a single dose of the short-acting 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) acts on gene expression from microdissected brain regions (anterior cingulate cortex - ACC; basolateral amygdala - BLA; ventral hippocampus CA1 region - vCA1 and dentate gyrus-DG) of naive and stressed mice. Specifically, we compared gene expression of Arc, Zif268, BDNF, CREB, mTORC1, NR2A, TRIP8b, and NFkB in mice injected with 5-MeO-DMT or saline at different time points (1 h, 5 h, or 5 days prior). 5-MeO-DMT altered mRNA expression of immediate early genes Arc and ZiF268 in the ACC, BLA, and vCA1, while NR2A expression was decreased after 5 h in the vCA1. We also found a long-term increase in TRIP8b, a gene related to the modulation of neuronal activity, in the vCA1 after 5 days. Behaviorally, 5-MeO-DMT treated mice showed mixed anxiolytic and anxiogenic effects in the elevated plus maze and open field test 24 h or 5 days after treatment. However, pre-treated mice subjected to acute stress showed both lower corticosterone levels and robust anxiolytic effects of 5-MeO-DMT administration. Together, our findings provide insights into the molecular actions of 5-MeO-DMT in the brain related to anxiolytic effects of behavior.

Biases arising from linked administrative data for epidemiological research: a conceptual framework from registration to analyses.

Linked administrative data offer a rich source of information that can be harnessed to describe patterns of disease, understand their causes and evaluate interventions. However, administrative data are primarily collected for operational reasons such as recording vital events for legal purposes, and planning, provision and monitoring of services. The processes involved in generating and linking administrative datasets may generate sources of bias that are often not adequately considered by researchers. We provide a framework describing these biases, drawing on our experiences of using the 100 Million Brazilian Cohort (100MCohort) which contains records of more than 131 million people whose families applied for social assistance between 2001 and 2018. Datasets for epidemiological research were derived by linking the 100MCohort to health-related databases such as the Mortality Information System and the Hospital Information System. Using the framework, we demonstrate how selection and misclassification biases may be introduced in three different stages: registering and recording of people's life events and use of services, linkage across administrative databases, and cleaning and coding of variables from derived datasets. Finally, we suggest eight recommendations which may reduce biases when analysing data from administrative sources.