Purpose of Review We present biological and psychological factors implicated in psychiatric manifestations of SARS-CoV-2, as well as its neuroinvasive capability and immune pathophysiology. Recent Findings Preexisting mental illness leads to worse clinical outcomes in COVID-19. The presence of the virus was reported in the cerebrospinal fluid (CSF) and brain tissue post-mortem. Most common psychiatric manifestations include delirium, mood disorders, anxiety disorders, and posttraumatic stress disorder. “Long-COVID” non-syndromal presentations include “brain-fogginess,” autonomic instability, fatigue, and insomnia. Summary SARS-CoV-2 infection can trigger prior vulnerabilities based on the priming of microglia and other cells, induced or perpetuated by aging and mental and physical illnesses. COVID-19 could further induce priming of neuroimmunological substrates leading to exacerbated immune response and autoimmunity targeting structures in the central nervous system (CNS), in response to minor immune activating environmental exposures, including stress, minor infections, allergens, pollutants, and traumatic brain injury.

ED patient response to the kiosk system was favorable. Subjects easily and quickly navigated the program, with the exception of a login screen, which could be eliminated via automated login using ID bracelet scanners.

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Objectives: To illustrate the use of machine learning methods to search for heterogeneous effects of a target modifiable risk factor on suicide in observational studies. The illustration focuses on secondary analysis of a matched case-control study of vitamin D deficiency predicting subsequent suicide. Methods:We describe a variety of machine learning methods to search for prescriptive predictors; that is, predictors of significant variation in the association between a target risk factor and subsequent suicide. In each case, the purpose is to evaluate the potential value of selective intervention on the target risk factor to prevent the outcome based on the provisional assumption that the target risk factor is causal. The approaches illustrated include risk modeling based on the super learner ensemble machine learning method, Least Absolute Shrinkage and Selection Operator (Lasso) penalized regression, and the causal forest algorithm. Results:The logic of estimating heterogeneous intervention effects is exposited along with the illustration of some widely used methods for implementing this logic.

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