Trauma and social pathways to psychosis: Examining the role of attachment, social rank and dissociation in a clinical sample.

Objectives: The trauma and social pathways model of psychosis proposes interactions between trauma, attachment, social rank and dissociation in pathways to psychosis, though this model has yet to be empirically investigated. The primary aim of this study was to examine the overall predictive value of the trauma and social pathways model using regression analysis. A secondary aim was to delineate hypothesized pathways between trauma and positive symptoms of psychosis using serial mediation analysis.

Biliary Polycyclic Aromatic Hydrocarbon Metabolite Equivalents Measured in Fish and Subtidal Invertebrates Following the Refugio Beach Oil Spill.

Monterey formation crude oil spilled from an onshore pipeline and entered the surf zone near Refugio State Beach, Santa Barbara County, CA, USA on 19 May 2015. Exposure to nearshore fish was evaluated by measuring biliary polycyclic aromatic hydrocarbon (PAH) metabolite concentrations in surfperch (Embiotocidae), collected near the release point, at a lesser oiled area, Gaviota State Beach, and near the Coal Oil Point oil seep, Campus Point, at both four days and approximately one year after the oil spill. Three to four weeks after the spill, fish, invertebrates, and kelp were collected near the same three sites for PAH analysis of edible tissues to support the fishery closure assessment.

Maternal depression trajectories and child BMI in a multi-ethnic sample: a latent growth modeling analysis.

Background: Perinatal (antenatal and postpartum) depression impacts approximately 12% of mothers. Perinatal depression can impact everyday functioning for mothers, and the relationship with, and development of, their children. The purpose of this study was to investigate depression trajectories from the antenatal period through 54-months postpartum and associations with child body mass index at 54-months postpartum.

Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study.

Background: Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature.

Objective: This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care.

Methods: In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis.