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

Mechanisms of Severe Acute Respiratory Syndrome Coronavirus-Induced Acute Lung Injury

ABSTRACT Systems biology offers considerable promise in uncovering novel pathways by which viruses and other microbial pathogens interact with host signaling and expression networks to mediate disease severity. In this study, we have developed an unbiased modeling approach to identify new pathways and network connections mediating acute lung injury, using severe acute respiratory syndrome coronavirus (SARS-CoV) as a model pathogen. We utilized a time course of matched virologic, pathological, and transcriptomic data within a novel methodological framework that can detect pathway enrichment among key highly connected network genes. This unbiased approach produced a high-priority list of 4 genes in one pathway out of over 3,500 genes that were differentially expressed following SARS-CoV infection. With these data, we predicted that the urokinase and other wound repair pathways would regulate lethal versus sublethal disease following SARS-CoV infection in mice. We validated the importance of the urokinase pathway for SARS-CoV disease severity using genetically defined knockout mice, proteomic correlates of pathway activation, and pathological disease severity. The results of these studies demonstrate that a fine balance exists between host coagulation and fibrinolysin pathways regulating pathological disease outcomes, including diffuse alveolar damage and acute lung injury, following infection with highly pathogenic respiratory viruses, such as SARS-CoV.IMPORTANCESevere acute respiratory syndrome coronavirus (SARS-CoV) emerged in 2002 and 2003, and infected patients developed an atypical pneumonia, acute lung injury (ALI), and acute respiratory distress syndrome (ARDS) leading to pulmonary fibrosis and death. We identified sets of differentially expressed genes that contribute to ALI and ARDS using lethal and sublethal SARS-CoV infection models. Mathematical prioritization of our gene sets identified the urokinase and extracellular matrix remodeling pathways as the most enriched pathways. By infecting Serpine1-knockout mice, we showed that the urokinase pathway had a significant effect on both lung pathology and overall SARS-CoV pathogenesis. These results demonstrate the effective use of unbiased modeling techniques for identification of high-priority host targets that regulate disease outcomes. Similar transcriptional signatures were noted in 1918 and 2009 H1N1 influenza virus-infected mice, suggesting a common, potentially treatable mechanism in development of virus-induced ALI

A comprehensive collection of systems biology data characterizing the host response to viral infection

The Systems Biology for Infectious Diseases Research program was established by the U.S. National Institute of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe acute respiratory syndrome coronavirus (SARS-CoV), were investigated. Study validation was demonstrated via experimental quality control measures and meta-analysis of independent experiments performed under similar conditions. Primary assay results are archived at the GEO and PeptideAtlas public repositories, while processed statistical results together with standardized metadata are publically available at the Influenza Research Database (www.fludb.org) and the Virus Pathogen Resource (www.viprbrc.org). By comparing data from mutant versus wild-type virus and host strains, RNA versus protein differential expression, and infection with genetically similar strains, these data can be used to further investigate genetic and physiological determinants of host responses to viral infection

Updating the approaches to define susceptibility and resistance to anti-tuberculosis agents: implications for diagnosis and treatment

11 páginas, 2 figuras, 1 tablaInappropriately high breakpoints have resulted in systematic false-susceptible AST results to anti-TB drugs. MIC, PK/PD and clinical outcome data should be combined when setting breakpoints to minimise the emergence and spread of antimicrobial resistance.I. Comas was supported by PID2019-104477RB-I00 from the Spanish Science Ministry and by ERC (CoG 101001038)Peer reviewe

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution.C.L. was supported by the Vienna Science and Technology Fund (WWTF VRG13-007); L.M.D. was supported by ERC 647910 (KINSHIP); D.I.B. and N.I. received funding from CONICET, Argentina; L.K., F.K. and Á. Putz were supported by the European Social Fund (EFOP-3.6.1.-16-2016-00004; ‘Comprehensive Development for Implementing Smart Specialization Strategies at the University of Pécs’). K.U. and E. Vergauwe were supported by a grant from the Swiss National Science Foundation (PZ00P1_154911 to E. Vergauwe). T.G. is supported by the Social Sciences and Humanities Research Council of Canada (SSHRC). M.A.V. was supported by grants 2016-T1/SOC-1395 (Comunidad de Madrid) and PSI2017-85159-P (AEI/FEDER UE). K.B. was supported by a grant from the National Science Centre, Poland (number 2015/19/D/HS6/00641). J. Bonick and J.W.L. were supported by the Joep Lange Institute. G.B. was supported by the Slovak Research and Development Agency (APVV-17-0418). H.I.J. and E.S. were supported by a French National Research Agency ‘Investissements d’Avenir’ programme grant (ANR-15-IDEX-02). T.D.G. was supported by an Australian Government Research Training Program Scholarship. The Raipur Group is thankful to: (1) the University Grants Commission, New Delhi, India for the research grants received through its SAP-DRS (Phase-III) scheme sanctioned to the School of Studies in Life Science; and (2) the Center for Translational Chronobiology at the School of Studies in Life Science, PRSU, Raipur, India for providing logistical support. K. Ask was supported by a small grant from the Department of Psychology, University of Gothenburg. Y.Q. was supported by grants from the Beijing Natural Science Foundation (5184035) and CAS Key Laboratory of Behavioral Science, Institute of Psychology. N.A.C. was supported by the National Science Foundation Graduate Research Fellowship (R010138018). We acknowledge the following research assistants: J. Muriithi and J. Ngugi (United States International University Africa); E. Adamo, D. Cafaro, V. Ciambrone, F. Dolce and E. Tolomeo (Magna Græcia University of Catanzaro); E. De Stefano (University of Padova); S. A. Escobar Abadia (University of Lincoln); L. E. Grimstad (Norwegian School of Economics (NHH)); L. C. Zamora (Franklin and Marshall College); R. E. Liang and R. C. Lo (Universiti Tunku Abdul Rahman); A. Short and L. Allen (Massey University, New Zealand), A. Ateş, E. Güneş and S. Can Özdemir (Boğaziçi University); I. Pedersen and T. Roos (Åbo Akademi University); N. Paetz (Escuela de Comunicación Mónica Herrera); J. Green (University of Gothenburg); M. Krainz (University of Vienna, Austria); and B. Todorova (University of Vienna, Austria). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.https://www.nature.com/nathumbehav/am2023BiochemistryGeneticsMicrobiology and Plant Patholog

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

On Revolutions

Sometimes the normal course of events is disrupted by a particularly swift and profound change. Historians have often referred to such changes as "revolutions" and, though they have identied many of them, they have rarely supported their claims with statistical evidence. Here we present a method to identify revolutions based on a measure of the multivariate rate of change called Foote Novelty. We dene revolutions as those periods of time when the value of this measure, F, can, by a non-parametric test, be shown to be signicantly greater than the background rate. Our method also identies conservative periods when the rate of change is unusually low. Importantly, our method permits searching for revolutions over any time scale that the data permit. We apply it to several quantitative data sets that capture long-term political, social and cultural changes and, in some of them, identify revolutions, both well known and not. Our method is a general one that can be applied to any phenomenon captured by multivariate time series data of sufficient quality.B.L. was supported by EPSRC grant code: EP/F500394/1. V-Dem data collection was supported by European Research Council, Grant 724191; Riksbankens Jubileumsfond, Grant M13-0559:1; Knut and Alice Wallenberg Foundation to Wallenberg Academy Fellow, Grant 2013.0166; as well as by internal grants from the Vice- Chancellor's o ffice, the Dean of the College of Social Sciences, and the Department of Political Science at University of Gothenburg. P.L. was also supported by the Marianne and Marcus Wallenbergs Foundation Grant 2017.004

Hangry Sea Anemones Takeover the Rocky Intertidal

The rocky intertidal zone is a paradigmatic ecosystem found along the coastline where the land meets the sea. It holds a plethora of unique animals that fill various niches, creating an atmosphere of constant interactions among both biotic and abiotic factors. The intertidal sea anemone, Anthopleura sola, (A. sola) is dispersed throughout our coastline, making it a widely abundant animal with — surprisingly — limited research done on the species. Our research proposes to examine the ecology of intraspecific aggression among A. sola and how their behaviors influence biological organization at both the individual and population levels. To further understand how habitat-specific factors influence spatial distribution, we will investigate agonistic behaviors that are likely to occur across two different habitats: cobble and bench. We define a cobble habitat as being of loose cobblestone and a bench habitat as a flat, continuous rock substrate. Previous analysis of these habitats from Coal Oil Point, Santa Barbara County, California, suggests that the spatial distribution is influenced by environmental factors and other population-specific characteristics (e.g. density) which may contextualize behavioral traits. A comparative analysis of agonistic behaviors and dietary components will be conducted to better understand how resource availability influences spatial competition. With this information, our study can provide ways to better mitigate the intertidal ecosystems as we experience an increase in environmental changes. This poster was presented at UCSB's 2024 undergraduate research symposium for the department of Ecology, Evolution, and Marine Biology, and received a best poster award

Generative networks for precision enthusiasts

Generative networks are opening new avenues in fast event generation for the LHC. We show how generative flow networks can reach percent-level precision for kinematic distributions, how they can be trained jointly with a discriminator, and how this discriminator improves the generation. Our joint training relies on a novel coupling of the two networks which does not require a Nash equilibrium. We then estimate the generation uncertainties through a Bayesian network setup and through conditional data augmentation, while the discriminator ensures that there are no systematic inconsistencies compared to the training data