Breakthrough SARS-CoV-2 infections after COVID-19 mRNA vaccination in MS patients on disease modifying therapies during the Delta and the Omicron waves in Italy

Background In this study we aimed to monitor the risk of breakthrough SARS-CoV-2 infection in patients with MS (pwMS) under different DMTs and to identify correlates of reduced protection.Methods This is a prospective Italian multicenter cohort study, long-term clinical follow-up of the CovaXiMS (Covid-19 vaccine in Multiple Sclerosis) study. 1855 pwMS scheduled for SARS-CoV-2 mRNA vaccination were enrolled and followed up to a mean time of 10 months. The cumulative incidence of breakthrough Covid-19 cases in pwMS was calculated before and after December 2021, to separate the Delta from the Omicron waves and to account for the advent of the third vaccine dose.Findings 1705 pwMS received 2 m-RNA vaccine doses, 21/28 days apart. Of them, 1508 (88.5%) had blood assessment 4 weeks after the second vaccine dose and 1154/1266 (92%) received the third dose after a mean interval of 210 days (range 90-342 days) after the second dose. During follow-up, 131 breakthrough Covid-19 infections (33 during the Delta and 98 during the Omicron wave) were observed. The probability to be infected during the Delta wave was associated with SARS-CoV-2 antibody levels measured after 4 weeks from the second vaccine dose (HR=0.57, p < 0.001); the protective role of antibodies was preserved over the whole follow up (HR=0.57, 95%CI=0.43-0.75, p < 0.001), with a significant reduction (HR=1.40, 95%CI=1.01-1.94, p=0.04) for the Omicron cases. The third dose significantly reduced the risk of infection (HR=0.44, 95%CI=0.21-0.90,p=0.025) during the Omicron wave.Interpretation The risk of breakthrough SARS-CoV-2 infections is mainly associated with reduced levels of the virus-specific humoral immune response. Copyright (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/

Data monitoring roadmap. The experience of the Italian Multiple Sclerosis and Related Disorders Register

Introduction Over the years, disease registers have been increasingly considered a source of reliable and valuable population studies. However, the validity and reliability of data from registers may be limited by missing data, selection bias or data quality not adequately evaluated or checked.This study reports the analysis of the consistency and completeness of the data in the Italian Multiple Sclerosis and Related Disorders Register.MethodsThe Register collects, through a standardized Web-based Application, unique patients.Data are exported bimonthly and evaluated to assess the updating and completeness, and to check the quality and consistency. Eight clinical indicators are evaluated.ResultsThe Register counts 77,628 patients registered by 126 centres. The number of centres has increased over time, as their capacity to collect patients.The percentages of updated patients (with at least one visit in the last 24 months) have increased from 33% (enrolment period 2000-2015) to 60% (enrolment period 2016-2022). In the cohort of patients registered after 2016, there were >= 75% updated patients in 30% of the small centres (33), in 9% of the medium centres (11), and in all the large centres (2).Clinical indicators show significant improvement for the active patients, expanded disability status scale every 6 months or once every 12 months, visits every 6 months, first visit within 1 year and MRI every 12 months.ConclusionsData from disease registers provide guidance for evidence-based health policies and research, so methods and strategies ensuring their quality and reliability are crucial and have several potential applications

Disease-Modifying Therapies and Coronavirus Disease 2019 Severity in Multiple Sclerosis

Objective: This study was undertaken to assess the impact of immunosuppressive and immunomodulatory therapies on the severity of coronavirus disease 2019 (COVID-19) in people with multiple sclerosis (PwMS). Methods: We retrospectively collected data of PwMS with suspected or confirmed COVID-19. All the patients had complete follow-up to death or recovery. Severe COVID-19 was defined by a 3-level variable: mild disease not requiring hospitalization versus pneumonia or hospitalization versus intensive care unit (ICU) admission or death. We evaluated baseline characteristics and MS therapies associated with severe COVID-19 by multivariate and propensity score (PS)-weighted ordinal logistic models. Sensitivity analyses were run to confirm the results. Results: Of 844 PwMS with suspected (n = 565) or confirmed (n = 279) COVID-19, 13 (1.54%) died; 11 of them were in a progressive MS phase, and 8 were without any therapy. Thirty-eight (4.5%) were admitted to an ICU; 99 (11.7%) had radiologically documented pneumonia; 96 (11.4%) were hospitalized. After adjusting for region, age, sex, progressive MS course, Expanded Disability Status Scale, disease duration, body mass index, comorbidities, and recent methylprednisolone use, therapy with an anti-CD20 agent (ocrelizumab or rituximab) was significantly associated (odds ratio [OR] = 2.37, 95% confidence interval [CI] = 1.18\u20134.74, p = 0.015) with increased risk of severe COVID-19. Recent use (<1 month) of methylprednisolone was also associated with a worse outcome (OR = 5.24, 95% CI = 2.20\u201312.53, p = 0.001). Results were confirmed by the PS-weighted analysis and by all the sensitivity analyses. Interpretation: This study showed an acceptable level of safety of therapies with a broad array of mechanisms of action. However, some specific elements of risk emerged. These will need to be considered while the COVID-19 pandemic persists. ANN NEUROL 2021;89:780\u2013789

Shattered pellet injection experiments at JET in support of the ITER disruption mitigation system design

A series of experiments have been executed at JET to assess the efficacy of the newly installed shattered pellet injection (SPI) system in mitigating the effects of disruptions. Issues, important for the ITER disruption mitigation system, such as thermal load mitigation, avoidance of runaway electron (RE) formation, radiation asymmetries during thermal quench mitigation, electromagnetic load control and RE energy dissipation have been addressed over a large parameter range. The efficiency of the mitigation has been examined for the various SPI injection strategies. The paper summarises the results from these JET SPI experiments and discusses their implications for the ITER disruption mitigation scheme

First-Principles Density Limit Scaling in Tokamaks Based on Edge Turbulent Transport and Implications for ITER

A first-principles scaling law, based on turbulent transport considerations, and a multimachine database of density limit discharges from the ASDEX Upgrade, JET, and TCV tokamaks, show that the increase of the boundary turbulent transport with the plasma collisionality sets the maximum density achievable in tokamaks. This scaling law shows a strong dependence on the heating power, therefore predicting for ITER a significantly larger safety margin than the Greenwald empirical scaling [Greenwald et al., Nucl. Fusion, 28, 2199 (1988)] in case of unintentional high-to-low confinement transition

New H-mode regimes with small ELMs and high thermal confinement in the Joint European Torus

New H-mode regimes with high confinement, low core impurity accumulation, and small edge-localized mode perturbations have been obtained in magnetically confined plasmas at the Joint European Torus tokamak. Such regimes are achieved by means of optimized particle fueling conditions at high input power, current, and magnetic field, which lead to a self-organized state with a strong increase in rotation and ion temperature and a decrease in the edge density. An interplay between core and edge plasma regions leads to reduced turbulence levels and outward impurity convection. These results pave the way to an attractive alternative to the standard plasmas considered for fusion energy generation in a tokamak with a metallic wall environment such as the ones expected in ITER.& nbsp;Published under an exclusive license by AIP Publishing

Overview of JET results for optimising ITER operation

The JET 2019–2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major neutral beam injection upgrade providing record power in 2019–2020, and tested the technical and procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle (α) physics in the coming D–T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed shattered pellet injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design and operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D–T benefited from the highest D–D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER

Testing a prediction model for the H-mode density pedestal against JET-ILW pedestals

The neutral ionisation model proposed by Groebner et al (2002 Phys. Plasmas 9 2134) to determine the plasma density profile in the H-mode pedestal, is extended to include charge exchange processes in the pedestal stimulated by the ideas of Mahdavi et al (2003 Phys. Plasmas 10 3984). The model is then tested against JET H-mode pedestal data, both in a 'standalone' version using experimental temperature profiles and also by incorporating it in the Europed version of EPED. The model is able to predict the density pedestal over a wide range of conditions with good accuracy. It is also able to predict the experimentally observed isotope effect on the density pedestal that eludes simpler neutral ionization models

Comparing pedestal structure in JET-ILW H-mode plasmas with a model for stiff ETG turbulent heat transport

A predictive model for the electron temperature profile of the H-mode pedestal is described, and its results are compared with the pedestal structure of JET-ILW plasmas. The model is based on a scaling for the gyro-Bohm normalized, turbulent electron heat flux qe/qe,gB resulting from electron temperature gradient (ETG) turbulence, derived from results of nonlinear gyrokinetic (GK) calculations for the steep gradient region. By using the local temperature gradient scale length L-Te in the normalization, the dependence of q(e)/q(e,g)B on the normalized gradients R/L-Te and R/(Lne) can be represented by a unified scaling with the parameter eta(e) = L-ne/L-Te, to which the linear stability of ETG turbulence is sensitive when the density gradient is sufficiently steep. For a prescribed density profile, the value of R/L-Te determined from this scaling, required to maintain a constant electron heat flux qe across the pedestal, is used to calculate the temperature profile. Reasonable agreement with measurements is found for different cases, the model providing an explanation of the relative widths and shifts of the T-e and n(e) profiles, as well as highlighting the importance of the separatrix boundary conditions. Other cases showing disagreement indicate conditions where other branches of turbulence might dominate.This article is part of a discussion meeting issue "H-mode transition and pedestal studies in fusion plasmas'

Disruption prediction at JET through deep convolutional neural networks using spatiotemporal information from plasma profiles

In view of the future high power nuclear fusion experiments, the early identification of disruptions is a mandatory requirement, and presently the main goal is moving from the disruption mitigation to disruption avoidance and control. In this work, a deep-convolutional neural network (CNN) is proposed to provide early detection of disruptive events at JET. The CNN ability to learn relevant features, avoiding hand-engineered feature extraction, has been exploited to extract the spatiotemporal information from 1D plasma profiles. The model is trained with regularly terminated discharges and automatically selected disruptive phase of disruptions, coming from the recent ITER-like-wall experiments. The prediction performance is evaluated using a set of discharges representative of different operating scenarios, and an in-depth analysis is made to evaluate the performance evolution with respect to the considered experimental conditions. Finally, as real-time triggers and termination schemes are being developed at JET, the proposed model has been tested on a set of recent experiments dedicated to plasma termination for disruption avoidance and mitigation. The CNN model demonstrates very high performance, and the exploitation of 1D plasma profiles as model input allows us to understand the underlying physical phenomena behind the predictor decision