Effect of Acute Ozone Induced Airway Inflammation on Human Sympathetic Nerve Traffic: A Randomized, Placebo Controlled, Crossover Study

Background: Ozone concentrations in ambient air are related to cardiopulmonary perturbations in the aging population. Increased central sympathetic nerve activity induced by local airway inflammation may be one possible mechanism. Methodology/Principal Findings: To elucidate this issue further, we performed a randomized, double-blind, cross-over study, including 14 healthy subjects (3 females, age 22-47 years), who underwent a 3 h exposure with intermittent exercise to either ozone (250 ppb) or clean air. Induced sputum was collected 3 h after exposure. Nineteen to 22 hours after exposure, we recorded ECG, finger blood pressure, brachial blood pressure, respiration, cardiac output, and muscle sympathetic nerve activity (MSNA) at rest, during deep breathing, maximum-inspiratory breath hold, and a Valsalva maneuver. While the ozone exposure induced the expected airway inflammation, as indicated by a significant increase in sputum neutrophils, we did not detect a significant estimated treatment effect adjusted for period on cardiovascular measurements. Resting heart rate (clean air: 59 +/- 62, ozone 60 +/- 62 bpm), blood pressure (clean air: 121 +/- 3/71 +/- 2 mmHg; ozone: 121 +/- 2/71 +/- 2mmHg), cardiac output (clean air: 7.42 +/- 0.29 mmHg; ozone: 7.98 +/- 0.60 l/min), and plasma norepinephrine levels (clean air: 213 +/- 21 pg/ml; ozone: 202 +/- 16 pg/ml), were similar on both study days. No difference of resting MSNA was observed between ozone and air exposure (air: 2362, ozone: 2362 bursts/min). Maximum MSNA obtained at the end of apnea (air: 44 +/- 4, ozone: 48 +/- 4 bursts/min) and during the phase II of the Valsalva maneuver (air: 64 +/- 5, ozone: 57 +/- 6 bursts/min) was similar. Conclusions/Significance: Our study suggests that acute ozone-induced airway inflammation does not increase resting sympathetic nerve traffic in healthy subjects, an observation that is relevant for environmental health. However, we can not exclude that chronic airway inflammation may contribute to sympathetic activation

Rationale and design of the RIACT–study: a multi-center placebo controlled double blind study to test the efficacy of RItuximab in Acute Cellular tubulointerstitial rejection with B-cell infiltrates in renal Transplant patients: study protocol for a randomized controlled trial

BACKGROUND: Acute kidney allograft rejection is a major cause for declining graft function and has a negative impact on the long-term graft survival. The majority (90%) of acute rejections are T-cell mediated and, therefore, the anti-rejection therapy targets T-cell-mediated mechanisms of the rejection process. However, there is increasing evidence that intragraft B-cells are also important in the T-cell-mediated rejections. First, a significant proportion of patients with acute T-cell-mediated rejection have B-cells present in the infiltrates. Second, the outcome of these patients is inferior, which has been related to an inferior response to the conventional anti-rejection therapy. Third, treatment of these patients with an anti-CD20 antibody (rituximab) improves the allograft outcome as reported in single case observations and in one small study. Despite the promise of these observations, solid evidence is required before incorporating this treatment option into a general treatment recommendation. METHODS/DESIGN: The RIACT study is designed as a randomized, double-blind, placebo-controlled, parallel group multicenter Phase III study. The study examines whether rituximab, in addition to the standard treatment with steroid-boli, leads to an improved one-year kidney allograft function, compared to the standard treatment alone in patients with acute T-cell mediated tubulointerstitial rejection and significant B-cell infiltrates in their biopsies. A total of 180 patients will be recruited. DISCUSSION: It is important to clarify the relevance of anti-B cell targeting in T-cell mediated rejection and answer the question whether this novel concept should be incorporated in the conventional anti-rejection therapy. TRIAL REGISTRATION: Clinical trials gov. number: NCT0111766

Web 2.0-Anwendungen in den Kommunen des Landes Sachsen-Anhalt

Der vorliegende Bericht ist das Ergebnis eines einjährigen Studienprojekts am Fachbereich Verwaltungswissenschaften der Hochschule Harz, in dem der derzeitige Einsatz und die potenziellen Einsatzmöglichkeiten von Web 2.0-Anwendungen in den Kommunen des Landes Sachsen-Anhalts untersucht wurden. Im Rahmen des Projekts wurden alle 134 Kommunen des Landes in einem Online-Survey befragt, darüber hinaus wurden in drei Kommunen vertie-fende Interviews durchgeführt und einzelne Aspekte der Web 2.0-Nutzung in einem Online-Forum mit Verwaltungsbeschäftigten und weiteren Expertinnen und Experten diskutiert. Insgesamt ist festzustellen, dass bislang nur ein Bruchteil der Kommunen in Sachsen-Anhalt - wir gehen von maximal 25% aus - Web 2.0-Anwendungen nutzt. Am Stärksten werden bis-lang offene, privatwirtschaftlich betriebene soziale Netzwerke, wie zum Beispiel Facebook, eingesetzt. Auf Basis der vorliegenden Ergebnisse muss bezweifelt werden, dass Kommunen von der Nutzung konventioneller sozialer Netzwerke grundsätzlich in einem Maß profitieren können, das den notwendigen Aufwand rechtfertigen würde. Kommunen, die entsprechende Anwen-dungen nutzen, sehen zwar zumindest teilweise einen Mehrwert. Die Argumente sind aber überwiegend sehr subjektiv. Die Nutzung konventioneller sozialer Netzwerke, wie zum Beispiel Facebook, kann daher nicht pauschal empfohlen werden. Dagegen liegt mit sogenannten Anliegenmanagementsystemen ein Beispiel für eine spezifisch von Kommunen nutzbare Web 2.0-Anwendung vor, aus dem auch Konsequenzen für weiter-führende Nutzungsformen und Neuentwicklungen gezogen werden können. Spezifisch für kommunale Verwaltungen entwickelte, kooperativ implementiert und genutzte Anwendungen, die eine elektronische Kollaboration zwischen Verwaltungen und Stakeholdern in Kernprozes-sen der Kommunalverwaltung ermöglichen, scheinen eine durchaus erfolgsversprechende Option. Neben einer thematischen Erweiterung auf allgemeine und alltägliche Themen der Interaktion von Bürgerinnen und Bürgern mit der kommunalen Verwaltung müssen auch die kooperativen Strukturen zur Entwicklung und zum Betrieb entsprechender Lösungen noch weiter entwickelt werden

Assessment of Brain Age in Posttraumatic Stress Disorder: Findings from the ENIGMA PTSD and Brain Age Working Groups

Background Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. Method Adult subjects (N = 2229; 56.2% male) aged 18–69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age − chronological age) controlling for chronological age, sex, and scan site. Results BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. Discussion Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan

Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder:Results from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). METHODS: Neuroimaging and clinical data were aggregated from 29 research sites in >1,300 PTSD cases and >2,000 trauma-exposed controls (age 6.2-85.2 years) by the ENIGMA-PGC PTSD working group. Cortical regions in the network were rank-ordered by effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2 to 148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared to the mean SC of 5,000 randomly generated n-region networks. RESULTS: Patients with PTSD, relative to non-PTSD controls, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex and age modulated covariance differences of PTSD-related structural networks. CONCLUSIONS: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The structural covariance networks that are perturbed in PTSD comport with converging evidence from resting state functional connectivity networks and networks impacted by inflammatory processes, and stress hormones in PTSD

A Comparison of Methods to Harmonize Cortical Thickness Measurements Across Scanners and Sites

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME INT), (2) LME that models both site-specific random intercepts and age-related random slopes (LME INT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ 2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects

Smaller total and subregional cerebellar volumes in posttraumatic stress disorder:a mega-analysis by the ENIGMA-PGC PTSD workgroup

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p -FDR &lt; 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.</p

Smaller total and subregional cerebellar volumes in posttraumatic stress disorder:a mega-analysis by the ENIGMA-PGC PTSD workgroup

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p -FDR &lt; 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.</p

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable