Acute Traumatic Stress Screening Can Identify Patients and Their Partners at Risk for Posttraumatic Stress Disorder Symptoms After a Cardiac Arrest:A Multicenter Prospective Cohort Study

BACKGROUND: Posttraumatic stress disorder (PTSD) is prevalent in patients who have had a cardiac arrest and their partners. Accordingly, acute traumatic stress screening is recommended, but its association with later PTSD symptoms has never been addressed in postresuscitation settings. OBJECTIVE: The aim of this study was to examine whether acute traumatic stress is associated with PTSD symptoms in patients who have had a cardiac arrest and their partners. METHODS: This multicenter longitudinal study of 141 patients and 97 partners measures acute traumatic stress at 3 weeks and PTSD symptoms at 3 months and 1 year after resuscitation, using the Impact of Event Scale. Linear regression models were used to evaluate the association between severity of acute traumatic stress and PTSD symptoms and post hoc to explore effects of group (patients/partners), age, and sex on acute traumatic stress severity. We categorized Impact of Event Scale scores higher than 26 at 3 months and 1 year as clinical severe PTSD symptoms. RESULTS: Higher acute traumatic stress severity is significantly positively associated with higher PTSD symptom severity at 3 months (patients and partners: P < .001) and 1 year (patients and partners: P < .001) postresuscitation, with the strongest association for women compared with men (P = .03). Acute traumatic stress was higher in women compared with men across groups (P = .02). Clinical severe PTSD symptoms were present in 26% to 28% of patients and 45% to 48% of partners. CONCLUSION: Experiencing a cardiac arrest may elicit clinical severe PTSD symptoms in patients, but particularly in their partners. Screening patients and partners for acute traumatic stress postresuscitation is warranted to identify those at increased risk of long-term PTSD symptoms

Brain serotonin 4 receptor binding is inversely associated with verbal memory recall

BACKGROUND: We have previously identified an inverse relationship between cerebral serotonin 4 receptor (5‐HT (4)R) binding and nonaffective episodic memory in healthy individuals. Here, we investigate in a novel sample if the association is related to affective components of memory, by examining the association between cerebral 5‐HT (4)R binding and affective verbal memory recall. METHODS: Twenty‐four healthy volunteers were scanned with the 5‐HT (4)R radioligand [(11)C]SB207145 and positron emission tomography, and were tested with the Verbal Affective Memory Test‐24. The association between 5‐HT (4)R binding and affective verbal memory was evaluated using a linear latent variable structural equation model. RESULTS: We observed a significant inverse association across all regions between 5‐HT (4)R binding and affective verbal memory performances for positive (p = 5.5 × 10(−4)) and neutral (p = .004) word recall, and an inverse but nonsignificant association for negative (p = .07) word recall. Differences in the associations with 5‐HT (4)R binding between word categories (i.e., positive, negative, and neutral) did not reach statistical significance. CONCLUSION: Our findings replicate our previous observation of a negative association between 5‐HT (4)R binding and memory performance in an independent cohort and provide novel evidence linking 5‐HT (4)R binding, as a biomarker for synaptic 5‐HT levels, to the mnestic processing of positive and neutral word stimuli in healthy humans

Discrete molecular dynamics can predict helical prestructured motifs in disordered proteins.

Intrinsically disordered proteins (IDPs) lack a stable tertiary structure, but their short binding regions termed Pre-Structured Motifs (PreSMo) can form transient secondary structure elements in solution. Although disordered proteins are crucial in many biological processes and designing strategies to modulate their function is highly important, both experimental and computational tools to describe their conformational ensembles and the initial steps of folding are sparse. Here we report that discrete molecular dynamics (DMD) simulations combined with replica exchange (RX) method efficiently samples the conformational space and detects regions populating alpha-helical conformational states in disordered protein regions. While the available computational methods predict secondary structural propensities in IDPs based on the observation of protein-protein interactions, our ab initio method rests on physical principles of protein folding and dynamics. We show that RX-DMD predicts alpha-PreSMos with high confidence confirmed by comparison to experimental NMR data. Moreover, the method also can dissect alpha-PreSMos in close vicinity to each other and indicate helix stability. Importantly, simulations with disordered regions forming helices in X-ray structures of complexes indicate that a preformed helix is frequently the binding element itself, while in other cases it may have a role in initiating the binding process. Our results indicate that RX-DMD provides a breakthrough in the structural and dynamical characterization of disordered proteins by generating the structural ensembles of IDPs even when experimental data are not available

Farseer-NMR: automatic treatment, analysis and plotting of large, multi-variable NMR data

We present Farseer-NMR (https://git.io/vAueU), a software package to treat, evaluate and combine NMR spectroscopic data from sets of protein-derived peaklists covering a range of experimental conditions. The combined advances in NMR and molecular biology enable the study of complex biomolecular systems such as flexible proteins or large multibody complexes, which display a strong and functionally relevant response to their environmental conditions, e.g. the presence of ligands, site-directed mutations, post translational modifications, molecular crowders or the chemical composition of the solution. These advances have created a growing need to analyse those systems’ responses to multiple variables. The combined analysis of NMR peaklists from large and multivariable datasets has become a new bottleneck in the NMR analysis pipeline, whereby information-rich NMR-derived parameters have to be manually generated, which can be tedious, repetitive and prone to human error, or even unfeasible for very large datasets. There is a persistent gap in the development and distribution of software focused on peaklist treatment, analysis and representation, and specifically able to handle large multivariable datasets, which are becoming more commonplace. In this regard, Farseer-NMR aims to close this longstanding gap in the automated NMR user pipeline and, altogether, reduce the time burden of analysis of large sets of peaklists from days/weeks to seconds/minutes. We have implemented some of the most common, as well as new, routines for calculation of NMR parameters and several publication-quality plotting templates to improve NMR data representation. Farseer-NMR has been written entirely in Python and its modular code base enables facile extension