The Early Bronze Age II City-Gate at Khirbet al-Batrawy, Jordan

Rapporto sui risultati degli scavi a Khirbet al-BatrawyReport on the results of the excavations at Khirbet al-Batraw

Inhibition of Natural Killer Cells through Engagement of CD81 by the Major Hepatitis C Virus Envelope Protein

The immune response against hepatitis C virus (HCV) is rarely effective at clearing the virus, resulting in ∼170 million chronic HCV infections worldwide. Here we report that ligation of an HCV receptor (CD81) inhibits natural killer (NK) cells. Cross-linking of CD81 by the major envelope protein of HCV (HCV-E2) or anti-CD81 antibodies blocks NK cell activation, cytokine production, cytotoxic granule release, and proliferation. This inhibitory effect was observed using both activated and resting NK cells. Conversely, on NK-like T cell clones, including those expressing NK cell inhibitory receptors, CD81 ligation delivered a costimulatory signal. Engagement of CD81 on NK cells blocks tyrosine phosphorylation through a mechanism which is distinct from the negative signaling pathways associated with NK cell inhibitory receptors for major histocompatibility complex class I. These results implicate HCV-E2–mediated inhibition of NK cells as an efficient HCV evasion strategy targeting the early antiviral activities of NK cells and allowing the virus to establish itself as a chronic infection

Contribution of CACNA1H Variants in Autism Spectrum Disorder Susceptibility

Autism Spectrum Disorder (ASD) is a highly heterogeneous neuropsychiatric disorder with a strong genetic component. The genetic architecture is complex, consisting of a combination of common low-risk and more penetrant rare variants. Voltage-gated calcium channels (VGCCs or Cav) genes have been implicated as high-confidence susceptibility genes for ASD, in accordance with the relevant role of calcium signaling in neuronal function. In order to further investigate the involvement of VGCCs rare variants in ASD susceptibility, we performed whole genome sequencing analysis in a cohort of 105 families, composed of 124 ASD individuals, 210 parents and 58 unaffected siblings. We identified 53 rare inherited damaging variants in Cav genes, including genes coding for the principal subunit and genes coding for the auxiliary subunits, in 40 ASD families. Interestingly, biallelic rare damaging missense variants were detected in the CACNA1H gene, coding for the T-type Cav3.2 channel, in ASD probands from two different families. Thus, to clarify the role of these CACNA1H variants on calcium channel activity we performed electrophysiological analysis using whole-cell patch clamp technology. Three out of four tested variants were shown to mildly affect Cav3.2 channel current density and activation properties, possibly leading to a dysregulation of intracellular Ca2+ ions homeostasis, thus altering calcium-dependent neuronal processes and contributing to ASD etiology in these families. Our results provide further support for the role of CACNA1H in neurodevelopmental disorders and suggest that rare CACNA1H variants may be involved in ASD development, providing a high-risk genetic background

Multiple-view diffuse optical tomography system based on time-domain compressive measurements

Compressive sensing is a powerful tool to efficiently acquire and reconstruct an image even in diffuse optical tomography (DOT) applications. In this work, a time-resolved DOT system based on structured light illumination, compressive detection, and multiple view acquisition has been proposed and experimentally validated on a biological tissue-mimicking phantom. The experimental scheme is based on two digital micromirror devices for illumination and detection modulation, in combination with a time-resolved single element detector. We fully validated the method and demonstrated both the imaging and tomographic capabilities of the system, providing state-of-the-art reconstruction quality

Multimodality Imaging in Cardiomyopathies with Hypertrophic Phenotypes

Multimodality imaging is a comprehensive strategy to investigate left ventricular hypertrophy (LVH), providing morphologic, functional, and often clinical information to clinicians. Hypertrophic cardiomyopathy (HCM) is defined by an increased LV wall thickness not only explainable by abnormal loading conditions. In the context of HCM, multimodality imaging, by different imaging techniques, such as echocardiography, cardiac magnetic resonance, cardiac computer tomography, and cardiac nuclear imaging, provides essential information for diagnosis, sudden cardiac death stratification, and management. Furthermore, it is essential to uncover the specific cause of HCM, such as Fabry disease and cardiac amyloidosis, which can benefit of specific treatments. This review aims to elucidate the current role of multimodality imaging in adult patients with HCM

Genomic history of Neolithic to Bronze Age Anatolia, Northern Levant, and Southern Caucasus

Here, we report genome-wide data analyses from 110 ancient Near Eastern individuals spanning the Late Neolithic to Late Bronze Age, a period characterized by intense interregional interactions for the Near East. We find that 6th millennium BCE populations of North/Central Anatolia and the Southern Caucasus shared mixed ancestry on a genetic cline that formed during the Neolithic between Western Anatolia and regions in today’s Southern Caucasus/Zagros. During the Late Chalcolithic and/or the Early Bronze Age, more than half of the Northern Levantine gene pool was replaced, while in the rest of Anatolia and the Southern Caucasus, we document genetic continuity with only transient gene flow. Additionally, we reveal a genetically distinct individual within the Late Bronze Age Northern Levant. Overall, our study uncovers multiple scales of population dynamics through time, from extensive admixture during the Neolithic period to long-distance mobility within the globalized societies of the Late Bronze Age. Video Abstrac

Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium

Introduction Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR. Methods We combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction. Results Our sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %). Implications Our findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.Funding: This work was supported by a Stratified Medicine Programme grant to JHM from the Medical Research Council (grant number MR/L011794/1 which funded the research and supported S.E.S., D.A., A.F.P, L.K., R.M.M., D.S., J.T.R.W, & J.H.M.); funding from the National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust and King's College London to D.A. and D.S; and funding from the Collaboration for Leadership in Applied Health Research and Care (CLAHRC) South London at King's College Hospital National Health Service Foundation Trust to S.E.S. The views expressed are those of the author(s) and not necessarily those of the Medical Research Council, National Health Service, the National Institute for Health Research, or the Department of Health. The AESOP (London, UK) cohort was funded by the UK Medical Research Council (Ref: G0500817). The Belfast (UK) cohort was funded by the Research and Development Office of Northern Ireland. The Bologna (Italy) cohort was funded by the European Community's Seventh Framework Program under grant agreement (agreement No.HEALTH-F2-2010–241909, Project EU-GEI). The GAP (London, UK) cohort was funded by the UK National Institute of Health Research(NIHR) Specialist Biomedical Research Centre for Mental Health, South London and Maudsley NHS Mental Health Foundation Trust (SLaM) and the Institute of Psychiatry, Psychology, and Neuroscience at King's College London; Psychiatry Research Trust; Maudsley Charity Research Fund; and the European Community's Seventh Framework Program grant (agreement No. HEALTH-F2-2009-241909, Project EU-GEI). The Lausanne (Switzerland) cohort was funded by the Swiss National Science Foundation (no. 320030_135736/1 to P.C. and K.Q.D., no 320030-120686, 324730-144064 and 320030-173211 to C.B.E and P.C., and no 171804 to LA); National Center of Competence in Research (NCCR) “SYNAPSY - The Synaptic Bases of Mental Diseases” from the Swiss National Science Foundation (no 51AU40_125759 to PC and KQD); and Fondation Alamaya (to KQD). The Oslo (Norway) cohort was funded by the Research Council of Norway (#223273/F50, under the Centers of Excellence funding scheme, #300309, #283798) and the South-Eastern Norway Regional Health Authority (#2006233, #2006258, #2011085, #2014102, #2015088 to IM, #2017-112). The Paris (France) cohort was funded by European Community's Seventh Framework Program grant (agreement No. HEALTH-F2-2010–241909, Project EU-GEI). The Prague (Czech Republic) cohort was funded by the Ministry of Health of the Czech Republic (Grant Number: NU20-04-00393). The Santander (Spain) cohort was funded by the following grants (to B.C.F): Instituto de Salud Carlos III, FIS 00/3095, PI020499, PI050427, PI060507, Plan Nacional de Drogas Research Grant 2005-Orden sco/3246/2004, and SENY Fundatio Research Grant CI 2005-0308007, Fundacion Marques de Valdecilla A/02/07 and API07/011. SAF2016-76046-R and SAF2013-46292-R (MINECO and FEDER). The West London (UK) cohort was funded The Wellcome Trust (Grant Number: 042025; 052247; 064607)

Cognitive performance at first episode of psychosis and the relationship with future treatment resistance: Evidence from an international prospective cohort study

Background: Antipsychotic treatment resistance affects up to a third of individuals with schizophrenia, with recent research finding systematic biological differences between antipsychotic resistant and responsive patients. Our aim was to determine whether cognitive impairment at first episode significantly differs between future antipsychotic responders and resistant cases. Methods: Analysis of data from seven international cohorts of first-episode psychosis (FEP) with cognitive data at baseline (N = 683) and follow-up data on antipsychotic treatment response: 605 treatment responsive and 78 treatment resistant cases. Cognitive measures were grouped into seven cognitive domains based on the preexisting literature. We ran multiple imputation for missing data and used logistic regression to test for associations between cognitive performance at FEP and treatment resistant status at follow-up. Results: On average patients who were future classified as treatment resistant reported poorer performance across most cognitive domains at baseline. Univariate logistic regressions showed that antipsychotic treatment resistance cases had significantly poorer IQ/general cognitive functioning at FEP (OR = 0.70, p = .003). These findings remained significant after adjusting for additional variables in multivariable analyses (OR = 0.76, p = .049). Conclusions: Although replication in larger studies is required, it appears that deficits in IQ/general cognitive functioning at first episode are associated with future treatment resistance. Cognitive variables may be able to provide further insight into neurodevelopmental factors associated with treatment resistance or act as early predictors of treatment resistance, which could allow prompt identification of refractory illness and timely interventions.Funding: This work was supported by a Stratified Medicine Programme grant to J.H.M from the Medical Research Council (grant number MR/L011794/1 which funded the research and supported S.E.S., A.F.P., R.M.M., J.T.R.W. & J.H.M.) E.M’s PhD is funded by the MRC-doctoral training partnership studentship in Biomedical Sciences at King’s College London. J.H.M, E.K, R.M.M are part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. A.P.K. is funded by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. O.A. is further funded by an NIHR Post-Doctoral Fellowship (PDF2018-11-ST2-020). The views expressed are those of the authors and not necessarily those of the NHS, the MRC, the NIHR or the Department of Health. E.M.J. is supported by the UCL/UCLH Biomedical Research Centre. The AESOP (London, UK) cohort was funded by the UK Medical Research Council (Ref: G0500817). The Bologna (Italy) cohort was funded by the European Community’s Seventh Framework Program under grant agreement (agreement No. HEALTH-F2-2010–241909, Project EU-GEI). The GAP (London, UK) cohort was funded by the UK National Institute of Health Research (NIHR) Specialist Biomedical Research Centre for Mental Health, South London and Maudsley NHS Mental Health Foundation Trust (SLaM) and the Institute of Psychiatry, Psychology, and Neuroscience at King’s College London; Psychiatry Research Trust; Maudsley Charity Research Fund; and the European Community’s Seventh Framework Program grant (agreement No. HEALTH-F2-2009-241909, Project EU-GEI). The Oslo (Norway) cohort was funded by the Stiftelsen KG Jebsen, Research Council of Norway (#223273, under the Centers of Excellence funding scheme, and #300309, #283798) and the South-Eastern Norway Regional Health Authority (#2006233, #2006258, #2011085, #2014102, #2015088, #2017-112). The Paris (France) cohort was funded by European Community’s Seventh Framework Program grant (agreement No. HEALTHF2-2010–241909, Project EU-GEI). The Santander (Spain) cohort was funded by the following grants (to B.C.F): Instituto de Salud Carlos III, FIS 00/3095, PI020499, PI050427, PI060507, Plan Nacional de Drogas Research Grant 2005-Orden sco/3246/2004, and SENY Fundatio Research Grant CI 2005-0308007, Fundacion Marques de Valdecilla A/02/07 and API07/011. SAF2016-76046-R and SAF2013-46292-R (MINECO and FEDER). The West London (UK) cohort was funded The Wellcome Trust (Grant Numbers: 042025; 052247; 064607)

Development and Validation of Predictive Model for a Diagnosis of First Episode Psychosis Using the Multinational EU-GEI Case-control Study and Modern Statistical Learning Methods

Background and Hypothesis: It is argued that availability of diagnostic models will facilitate a more rapid identification of individuals who are at a higher risk of first episode psychosis (FEP). Therefore, we developed, evaluated, and validated a diagnostic risk estimation model to classify individual with FEP and controls across six countries. Study Design: We used data from a large multi-center study encompassing 2627 phenotypically well-defined participants (aged 18-64 years) recruited from six countries spanning 17 research sites, as part of the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions study. To build the diagnostic model and identify which of important factors for estimating an individual risk of FEP, we applied a binary logistic model with regularization by the least absolute shrinkage and selection operator. The model was validated employing the internal-external cross-validation approach. The model performance was assessed with the area under the receiver operating characteristic curve (AUROC), calibration, sensitivity, and specificity. Study Results: Having included preselected 22 predictor variables, the model was able to discriminate adults with FEP and controls with high accuracy across all six countries (rangesAUROC=0.84-0.86). Specificity (range=73.9-78.0%) and sensitivity (range=75.6-79.3%) were equally good, cumulatively indicating an excellent model accuracy; though, calibration slope for the diagnostic model showed a presence of some overfitting when applied specifically to participants from France, the UK, and The Netherlands. Conclusions: The new FEP model achieved a good discrimination and good calibration across six countries with different ethnic contributions supporting its robustness and good generalizability.</p