Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence

Question Can plasma proteomic biomarkers aid prediction of transition to psychotic disorder in people at clinical high risk (CHR) of psychosis and adolescent psychotic experiences in the general population? Findings In this diagnostic study of 133 individuals at CHR in EU-GEI and 121 individuals from the general population in ALSPAC, models were developed based on baseline proteomic data, with excellent predictive performance for transition to psychotic disorder in individuals at CHR. In a general population sample, models based on proteomic data at age 12 years had fair predictive performance for psychotic experiences at age 18 years. Meaning Predictive models based on proteomic biomarkers may contribute to personalized prognosis and stratification strategies in individuals at risk of psychosis. This diagnostic study investigates whether proteomic biomarkers may aid the prediction of transition to psychotic disorder in the clinical high-risk state and adolescent psychotic experiences in the general population. Importance Biomarkers that are predictive of outcomes in individuals at risk of psychosis would facilitate individualized prognosis and stratification strategies. Objective To investigate whether proteomic biomarkers may aid prediction of transition to psychotic disorder in the clinical high-risk (CHR) state and adolescent psychotic experiences (PEs) in the general population. Design, Setting, and Participants This diagnostic study comprised 2 case-control studies nested within the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) and the Avon Longitudinal Study of Parents and Children (ALSPAC). EU-GEI is an international multisite prospective study of participants at CHR referred from local mental health services. ALSPAC is a United Kingdom-based general population birth cohort. Included were EU-GEI participants who met CHR criteria at baseline and ALSPAC participants who did not report PEs at age 12 years. Data were analyzed from September 2018 to April 2020. Main Outcomes and Measures In EU-GEI, transition status was assessed by the Comprehensive Assessment of At-Risk Mental States or contact with clinical services. In ALSPAC, PEs at age 18 years were assessed using the Psychosis-Like Symptoms Interview. Proteomic data were obtained from mass spectrometry of baseline plasma samples in EU-GEI and plasma samples at age 12 years in ALSPAC. Support vector machine learning algorithms were used to develop predictive models. Results The EU-GEI subsample (133 participants at CHR (mean [SD] age, 22.6 [4.5] years; 68 [51.1%] male) comprised 49 (36.8%) who developed psychosis and 84 (63.2%) who did not. A model based on baseline clinical and proteomic data demonstrated excellent performance for prediction of transition outcome (area under the receiver operating characteristic curve [AUC], 0.95; positive predictive value [PPV], 75.0%; and negative predictive value [NPV], 98.6%). Functional analysis of differentially expressed proteins implicated the complement and coagulation cascade. A model based on the 10 most predictive proteins accurately predicted transition status in training (AUC, 0.99; PPV, 76.9%; and NPV, 100%) and test (AUC, 0.92; PPV, 81.8%; and NPV, 96.8%) data. The ALSPAC subsample (121 participants from the general population with plasma samples available at age 12 years (61 [50.4%] male) comprised 55 participants (45.5%) with PEs at age 18 years and 61 (50.4%) without PEs at age 18 years. A model using proteomic data at age 12 years predicted PEs at age 18 years, with an AUC of 0.74 (PPV, 67.8%; and NPV, 75.8%). Conclusions and Relevance In individuals at risk of psychosis, proteomic biomarkers may contribute to individualized prognosis and stratification strategies. These findings implicate early dysregulation of the complement and coagulation cascade in the development of psychosis outcomes

Monte Carlo studies for the optimisation of the Cherenkov Telescope Array layout

International audienceThe Cherenkov Telescope Array (CTA) is the major next-generation observatory for ground-based very-high-energy gamma-ray astronomy. It will improve the sensitivity of current ground-based instruments by a factor of five to twenty, depending on the energy, greatly improving both their angular and energy resolutions over four decades in energy (from 20 GeV to 300 TeV). This achievement will be possible by using tens of imaging Cherenkov telescopes of three successive sizes. They will be arranged into two arrays, one per hemisphere, located on the La Palma island (Spain) and in Paranal (Chile). We present here the optimised and final telescope arrays for both CTA sites, as well as their foreseen performance, resulting from the analysis of three different large-scale Monte Carlo productions

Science with the Cherenkov Telescope Array

The Cherenkov Telescope Array, CTA, will be the major global observatory forvery high energy gamma-ray astronomy over the next decade and beyond. Thescientific potential of CTA is extremely broad: from understanding the role ofrelativistic cosmic particles to the search for dark matter. CTA is an explorerof the extreme universe, probing environments from the immediate neighbourhoodof black holes to cosmic voids on the largest scales. Covering a huge range inphoton energy from 20 GeV to 300 TeV, CTA will improve on all aspects ofperformance with respect to current instruments. The observatory will operate arrays on sites in both hemispheres to providefull sky coverage and will hence maximize the potential for the rarestphenomena such as very nearby supernovae, gamma-ray bursts or gravitationalwave transients. With 99 telescopes on the southern site and 19 telescopes onthe northern site, flexible operation will be possible, with sub-arraysavailable for specific tasks. CTA will have important synergies with many ofthe new generation of major astronomical and astroparticle observatories.Multi-wavelength and multi-messenger approaches combining CTA data with thosefrom other instruments will lead to a deeper understanding of the broad-bandnon-thermal properties of target sources. The CTA Observatory will be operated as an open, proposal-driven observatory,with all data available on a public archive after a pre-defined proprietaryperiod. Scientists from institutions worldwide have combined together to formthe CTA Consortium. This Consortium has prepared a proposal for a CoreProgramme of highly motivated observations. The programme, encompassingapproximately 40% of the available observing time over the first ten years ofCTA operation, is made up of individual Key Science Projects (KSPs), which arepresented in this document