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This is the institutional repository of the University of Basel. It indexes most of the research and scholarly output of the university and offers in some cases permanent open and worldwide access to the full text of the publications.

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    Publication
    Transient enlargement of brain ventricles during relapsingremitting multiple sclerosis and experimental autoimmune encephalomyelitis
    (American Society for Clinical Investigation, 2020-11-05)
    Millward, JM
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    Ramos, Delgado P
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    Smorodchenko, A
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    Boehmert, L
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    Periquito, J
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    Reimann, HM
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    Prinz, C
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    Els, A
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    Scheel, M
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    Bellmann-Strobl, J
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    Waiczies, H
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    Infante-Duarte, C
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    Chien, C
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    Kuchling, J
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    Pohlmann, A
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    Zipp, F
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    Paul, F
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    Niendorf, T
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    Waiczies, S
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    The brain ventricles are part of the fluid compartments bridging the CNS with the periphery. Using MRI, we previously observed a pronounced increase in ventricle volume (VV) in the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS). Here, we examined VV changes in EAE and MS patients in longitudinal studies with frequent serial MRI scans. EAE mice underwent serial MRI for up to 2 months, with gadolinium contrast as a proxy of inflammation, confirmed by histopathology. We performed a time-series analysis of clinical and MRI data from a prior clinical trial in which RRMS patients underwent monthly MRI scans over 1 year. VV increased dramatically during preonset EAE, resolving upon clinical remission. VV changes coincided with blood-brain barrier disruption and inflammation. VV was normal at the termination of the experiment, when mice were still symptomatic. The majority of relapsing-remitting MS (RRMS) patients showed dynamic VV fluctuations. Patients with contracting VV had lower disease severity and a shorter duration. These changes demonstrate that VV does not necessarily expand irreversibly in MS but, over short time scales, can expand and contract. Frequent monitoring of VV in patients will be essential to disentangle the disease-related processes driving short-term VV oscillations from persistent expansion resulting from atrophy.
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    EEG microstates as biomarker for psychosis in ultra-high-risk patients
    Resting-state EEG microstates are brief (50-100 ms) periods, in which the spatial configuration of scalp global field power remains quasi-stable before rapidly shifting to another configuration. Changes in microstate parameters have been described in patients with psychotic disorders. These changes have also been observed in individuals with a clinical or genetic high risk, suggesting potential usefulness of EEG microstates as a biomarker for psychotic disorders. The present study aimed to investigate the potential of EEG microstates as biomarkers for psychotic disorders and future transition to psychosis in patients at ultra-high-risk (UHR). We used 19-channel clinical EEG recordings and orthogonal contrasts to compare temporal parameters of four normative microstate classes (A-D) between patients with first-episode psychosis (FEP; n = 29), UHR patients with (UHR-T; n = 20) and without (UHR-NT; n = 34) later transition to psychosis, and healthy controls (HC; n = 25). Microstate A was increased in patients (FEP & UHR-T & UHR- NT) compared to HC, suggesting an unspecific state biomarker of general psychopathology. Microstate B displayed a decrease in FEP compared to both UHR patient groups, and thus may represent a state biomarker specific to psychotic illness progression. Microstate D was significantly decreased in UHR-T compared to UHR-NT, suggesting its potential as a selective biomarker of future transition in UHR patients.
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    Effectiveness of real-time continuous glucose monitoring to improve glycaemic control and pregnancy outcome in patients with gestational diabetes mellitus: A study protocol for a randomised controlled trial
    (BMJ Publishing Group, 2020-11-30) ;
    Linder, T.
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    Eppel, D.
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    Weisshaupt, K.
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    Klapp, C
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    Schellong, K.
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    Henrich, W.
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    Yerlikaya-Schatten, G.
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    Rosicky, I.
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    Husslein, P.
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    Chalubinski, K.
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    Mittlbock, M.
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    Rust, P
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    Jendle, J.
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    Fehm, T
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    Icks, A.
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    Vomhof, M.
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    Greiner, G.G.
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    Szendrödi, J
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    Roden, M.
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    Tura, A.
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    Gobl, C.S.
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    Introduction Real-time continuous glucose monitoring (rt-CGM) informs users about current interstitial glucose levels and allows early detection of glycaemic excursions and timely adaptation by behavioural change or pharmacological intervention. Randomised controlled studies adequately powered to evaluate the impact of long-term application of rt-CGM systems on the reduction of adverse obstetric outcomes in women with gestational diabetes (GDM) are missing. We aim to assess differences in the proportion of large for gestational age newborns in women using rt-CGM as compared with women with self-monitored blood glucose (primary outcome). Rates of neonatal hypoglycaemia, caesarean section and shoulder dystocia are secondary outcomes. A comparison of glucose metabolism and quality of life during and after pregnancy completes the scope of this study. Methods and analysis Open-label multicentre randomised controlled trial with two parallel groups including 372 female patients with a recent diagnosis of GDM (between 24+0 until 31+6 weeks of gestation): 186 with rt-CGM (Dexcom G6) and 186 with self-monitored blood glucose (SMBG). Women with GDM will be consecutively recruited and randomised to rt-CGM or control (SMBG) group after a run-in period of 6-8 days. The third visit will be scheduled 8-10 days later and then every 2 weeks. At every visit, glucose measurements will be evaluated and all patients will be treated according to the standard care. The control group will receive a blinded CGM for 10 days between the second and third visit and between week 36+0 and 38+6. Cord blood will be sampled immediately after delivery. 48 hours after delivery neonatal biometry and maternal glycosylated haemoglobin A1c (HbA1c) will be assessed, and between weeks 8 and 16 after delivery all patients receive a re-examination of glucose metabolism including blinded CGM for 8-10 days. Ethics and dissemination This study received ethical approval from the main ethic committee in Vienna. Data will be presented at international conferences and published in peer-reviewed journals.
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    Cohort profile: Targeted antenatal screening for haemoglobinopathies in Basel
    (BMJ Publishing Groupsubscriptions@bmjgroup.com, 2020-07-22) ; ; ; ; ; ;
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    Purpose The pregnancy cohort was established to examine the prevalence and variety of haemoglobinopathies in a high-risk group of pregnant women. Participants The pregnancy cohort is located in the Department of Obstetrics and Antenatal Care, University Hospital of Basel. The pregnant women were recruited in the first trimester between June 2015 and May 2019. Family origin questionnaires were used to screen pregnant women for the risk of a haemoglobin variant. Based on the questionnaire, pregnant women were divided into two groups: women with a high risk and women with a low risk of a haemoglobin variant. In women with a high risk, red blood cell indices, iron status and chromatography were conducted. Findings to date 1785 pregnant women were recruited. Out of the 1785 women, 929 were identified as a part of the high-risk group. Due to the missing data of 74 pregnant women with a high risk, the final analysis was conducted in the remaining 855 women. The prevalence of haemoglobinopathies in the high-risk group was 14.5% (124/855). Future plans This cohort will be used to: (1) implement the screening in prenatal care in Basel; (2) recommend the screening among pregnant women with a high risk of a haemoglobin variant in Switzerland; (3) improve prenatal and neonatal care in patients with a haemoglobin variant; (4) examine adverse pregnancy outcomes in women with a haemoglobin variant and (5) reduce maternal and neonatal morbidity and mortality in the future. Trial registration number ClinicalTrials.gov Registry (NCT04029142).
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    Traces of Trauma: A Multivariate Pattern Analysis of Childhood Trauma, Brain Structure, and Clinical Phenotypes
    (Elsevier Inc., 2020-12-01)
    Popovic, D
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    Ruef, A
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    Dwyer, DB
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    Antonucci, LA
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    Eder, J
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    Sanfelici, R
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    Kambeitz-Ilankovic, L
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    Oztuerk, OF
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    Dong, MS
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    Paul, R
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    Paolini, M
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    Hedderich, D
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    Haidl, T
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    Kambeitz, J
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    Ruhrmann, S
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    Chisholm, K
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    Schultze-Lutter, F
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    Falkai, P
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    Pergola, G
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    Blasi, G
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    Bertolino, A
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    Lencer, R
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    Dannlowski, U
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    Upthegrove, R
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    Salokangas, RKR
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    Pantelis, C
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    Meisenzahl, E
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    Wood, SJ
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    Brambilla, P
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    Koutsouleris, N
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    Background: Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. Methods: We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. Results: We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. Conclusions: Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research.