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Abstract Multiple sclerosis (MS) is a complex chronic neuroinflammatory disease characterized by demyelination leading to neuronal dysfunction and neurodegeneration manifested by various neurological impairments. The endocannabinoid system (ECS) is a lipid signalling network, which plays multiple roles in the central nervous system and the periphery, including synaptic signal transmission and modulation of inflammation. The ECS has been identified as a potential target for the development of novel therapeutic interventions in MS patients. It remains unclear whether ECS-associated metabolites are changed in MS and could serve as biomarkers in blood or cerebrospinal fluid (CSF). In this retrospective study we applied targeted lipidomics to matching CSF and serum samples of 74 MS and 80 non-neuroinflammatory control patients. We found that MS-associated lipidomic changes overall did not coincide between CSF and serum. While glucocorticoids correlated positively, only the endocannabinoid (eCB) 2-arachidonoyl glycerol (2-AG) showed a weak positive correlation (r = 0.3, p

Abstract Background Treatment decisions for persons with relapsing–remitting multiple sclerosis (RRMS) rely on clinical and radiological disease activity, the benefit-harm profile of drug therapy, and preferences of patients and physicians. However, there is limited evidence to support evidence-based personalized decision-making on how to adapt disease-modifying therapy treatments targeting no evidence of disease activity, while achieving better patient-relevant outcomes, fewer adverse events, and improved care. Serum neurofilament light chain (sNfL) is a sensitive measure of disease activity that captures and prognosticates disease worsening in RRMS. sNfL might therefore be instrumental for a patient-tailored treatment adaptation. We aim to assess whether 6-monthly sNfL monitoring in addition to usual care improves patient-relevant outcomes compared to usual care alone. Methods Pragmatic multicenter, 1:1 randomized, platform trial embedded in the Swiss Multiple Sclerosis Cohort (SMSC). All patients with RRMS in the SMSC for ≥ 1 year are eligible. We plan to include 915 patients with RRMS, randomly allocated to two groups with different care strategies, one of them new (group A) and one of them usual care (group B). In group A, 6-monthly monitoring of sNfL will together with information on relapses, disability, and magnetic resonance imaging (MRI) inform personalized treatment decisions (e.g., escalation or de-escalation) supported by pre-specified algorithms. In group B, patients will receive usual care with their usual 6- or 12-monthly visits. Two primary outcomes will be used: (1) evidence of disease activity (EDA3: occurrence of relapses, disability worsening, or MRI activity) and (2) quality of life (MQoL-54) using 24-month follow-up. The new treatment strategy with sNfL will be considered superior to usual care if either more patients have no EDA3, or their health-related quality of life increases. Data collection will be embedded within the SMSC using established trial-level quality procedures. Discussion MultiSCRIPT aims to be a platform where research and care are optimally combined to generate evidence to inform personalized decision-making in usual care. This approach aims to foster better personalized treatment and care strategies, at low cost and with rapid translation to clinical practice. Trial registration ClinicalTrials.gov NCT06095271. Registered on October 23, 2023

Background: Reporting of sex-specific analyses in multiple sclerosis (MS) is sparse. Disability accrual results from relapses (relapse-associated worsening) and independent thereof (progression independent of relapses). Objectives: A population of MS patients during relapse treated per standard of care was analyzed for sex differences and short-term relapse outcome (3–6 months) as measured by Expanded Disability Status Scale (EDSS) change. Design: Single-center retrospective study. Methods: We analyzed 134 MS relapses between March 2016 and August 2020. All events required relapse treatment (steroids and/or plasma exchange). Demographic, disease, and paraclinical characteristics [cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI)] were displayed separated by sex. Multivariable linear regression was run to identify factors associated with short-term EDSS change. Results: Mean age at relapse was 38.4 years (95% confidence interval: 36.3–40.4) with a proportion of 71.6% women in our cohort. Smoking was more than twice as prevalent in men (65.8%) than women (32.3%). In- and after-relapse EDSSs were higher in men [men: 3.3 (2.8–3.9), women: 2.7 (2.4–3.0); men: 3.0 (1.3–3.6); women: 1.8 (1.5–2.1)] despite similar relapse intervention. Paraclinical parameters revealed no sex differences. Our primary model identified female sex, younger age, and higher EDSS at relapse to be associated with EDSS improvement. A higher immunoglobulin G (IgG) quotient (CSF/serum) was associated with poorer short-term outcome [mean days between first relapse treatment and last EDSS assessment 130.2 (79.3–181.0)]. Conclusion: Sex and gender differences are important in outcome analyses of MS relapses. Effective treatment regimens need to respect putative markers for a worse outcome to modify long-term prognosis such as clinical and demographic variables, complemented by intrathecal IgG synthesis. Prospective trials should be designed to address these differences and confirm our results.

A large number of disease-modifying immunotherapies are available for the treatment of people with multiple sclerosis. Many disease-modifying immunotherapies show scarce or no safety data in pregnancy and breastfeeding and are labeled as being contraindicated during these periods in the Swiss summary of product characteristics. Some disease-modifying immunotherapies also have restrictions for male patients. Hence, family planning should always be considered in treatment decisions. If clinically necessary, the continuation of immunotherapy during pregnancy can be considered for some substances. In these situations, the “Good Off-Label Use Practice”, careful consideration of the benefit–risk profile, and interprofessional cooperation between the treating neurologist, obstetrician–gynecologist, and pharmacist/pharmacologist, ideally with the involvement of experienced centers, is necessary. Here, we present an update on disease-modifying immunotherapies in multiple sclerosis with a focus on family planning, pregnancy, and breastfeeding and provide consensus recommendations of the Medico-Scientific Advisory Board of the Swiss Multiple Sclerosis Society, the Swiss Neurological Society, and the Swiss Society for Gynecology and Obstetrics (represented by the Academy of Fetomaternal Medicine). These unified national recommendations are necessary, as guidelines from other countries differ and because of separate approval/reimbursement situations in Switzerland.

The detection of contrast-enhancing lesions (CELs) is fundamental for the diagnosis and monitoring of patients with multiple sclerosis (MS). This task is time-consuming and suffers from high intra- and inter-rater variability in clinical practice. However, only a few studies proposed automatic approaches for CEL detection. This study aimed to develop a deep learning model that automatically detects and segments CELs in clinical Magnetic Resonance Imaging (MRI) scans. A 3D UNet-based network was trained with clinical MRI from the Swiss Multiple Sclerosis Cohort. The dataset comprised 372 scans from 280 MS patients: 162 showed at least one CEL, while 118 showed no CELs. The input dataset consisted of T1-weighted before and after gadolinium injection, and FLuid Attenuated Inversion Recovery images. The sampling strategy was based on a white matter lesion mask to confirm the existence of real contrast-enhancing lesions. To overcome the dataset imbalance, a weighted loss function was implemented. The Dice Score Coefficient and True Positive and False Positive Rates were 0.76, 0.93, and 0.02, respectively. Based on these results, the model developed in this study might well be considered for clinical decision support.

Background: Depression has a major impact on the disease burden of multiple sclerosis (MS). Analyses of overlapping MS and depression risk factors [smoking, vitamin D (25-OH-VD) and Epstein-Barr virus (EBV) infection] and sex, age, disease characteristics and neuroimaging features associated with depressive symptoms in early MS are scarce. Objectives: To assess an association of MS risk factors with depressive symptoms within the German NationMS cohort. Design: Cross-sectional analysis within a multicenter observational study. Methods: Baseline data of n = 781 adults with newly diagnosed clinically isolated syndrome or relapsing-remitting MS qualified for analysis. Global and region-specific magnetic resonance imaging (MRI)-volumetry parameters were available for n = 327 patients. Association of demographic factors, MS characteristics and risk factors [sex, age, smoking, disease course, presence of current relapse, expanded disability status scale (EDSS) score, fatigue (fatigue scale motor cognition), 25-OH-VD serum concentration, EBV nuclear antigen-1 IgG (EBNA1-IgG) serum levels] and depressive symptoms (Beck Depression Inventory-II, BDI-II) was tested as a primary outcome by multivariable linear regression. Non-parametric correlation and group comparison were performed for associations of MRI parameters and depressive symptoms. Results: Mean age was 34.3 years (95% confidence interval: 33.6–35.0). The female-to-male ratio was 2.3:1. At least minimal depressive symptoms (BDI-II > 8) were present in n = 256 (32.8%), 25-OH-VD deficiency (

Abstract The aim of our study was to investigate whether self-reported feeling of loneliness (FoL) and COVID-19-specific health anxiety were associated with the presence of depressive symptoms during the first coronavirus disease 2019 (COVID-19) wave. Questionnaires of 603 persons of the Swiss Multiple Sclerosis Registry (SMSR) were cross-sectionally analyzed using descriptive and multivariable regression methods. The survey response rate was 63.9%. Depressive symptoms were assessed by the Beck Depression Inventory-Fast Screen (BDI-FS). COVID-19-specific health anxiety and FoL were measured using two 5-item Likert scaled pertinent questions. High scoring FoL (2.52, 95% confidence interval (CI) (2.06—2.98)) and/or COVID-19 specific health anxiety (1.36, 95% CI (0.87–1.85)) were significantly associated with depressive symptoms. Further stratification analysis showed that the impact of FoL on depressive symptoms affected all age groups. However, it was more pronounced in younger PwMS, whereas an impact of COVID-19 specific health anxiety on depressive symptoms was particularly observed in middle-aged PwMS. FoL and COVID-19-specific health anxiety were age-dependently associated with depressive symptoms during the first COVID-19 wave in Switzerland. Our findings could guide physicians, health authorities, and self-help groups to better accompany PwMS in times of public health crises.

Introduction: Patients under immunotherapies were excluded from the pivotal trials of vaccinations against the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), and no population-level data on disease outcomes such as case fatality rates in relation to vaccination coverage exist. Our study aims to fill this gap by investigating whether CFRs in patients with immunotherapies decrease with increasing vaccination coverage in the total population.Methods: We combined aggregated open source data on COVID-19 vaccination coverage from “Our World in Data” with publicly available anonymized COVID-19 case reports from the FDA Adverse Event Reporting System to compute COVID-19 CFRs for patients under immunotherapy at different vaccination coverage levels in the total population. CFRs at different vaccination coverage levels were then compared to CFRs before vaccination campaign start.Results: While we found an overall decrease in CFRs on population level with increasing vaccination coverage, we found no decrease in people using anti-CD20 or glucocorticoids.Discussion: Risk-mitigation strategies on an individual- and population-level are thus still needed to lower the probability of fatal SARS-CoV2 infection for these vulnerable populations.

Our previous work demonstrated that vitamin D (VitD) reduces experimental autoimmune encephalomyelitis (EAE) disease severity in wild-type (WT) but not in T cell-specific glucocorticoid (GC) receptor (GR)-deficient (GRlck) mice. This study aimed to investigate the interplay between the GR- and VitD receptor (VDR) signaling. In vivo, we confirmed the involvement of the GR in the VitD-induced effects in EAE using WT and GRlck mice. Furthermore, we observed that VitD-enhanced T cell apoptosis and T regulatory cell differentiation are diminished in vitro in CD3+ T cells of GRlck but not WT mice. Mechanistically, VitD does not appear to signal directly via the GR, as it does not bind to the GR, does not induce its nuclear translocation, and does not modulate the expression of two GR-induced genes. However, we observed that VitD enhances VDR protein expression in CD3+ T cells from WT but not GRlck mice in vitro, that the GR and the VDR spatially co-localize after VitD treatment, and that VitD does not modulate the expression of two VDR-induced genes in the absence of the GR. Our data suggest that a functional GR, specifically in T cells, is required for the VDR to signal appropriately to mediate the therapeutic effects of VitD.

BackgroundThe increasing availability of “real-world” data in the form of written text holds promise for deepening our understanding of societal and health-related challenges. Textual data constitute a rich source of information, allowing the capture of lived experiences through a broad range of different sources of information (eg, content and emotional tone). Interviews are the “gold standard” for gaining qualitative insights into individual experiences and perspectives. However, conducting interviews on a large scale is not always feasible, and standardized quantitative assessment suitable for large-scale application may miss important information. Surveys that include open-text assessments can combine the advantages of both methods and are well suited for the application of natural language processing (NLP) methods. While innovations in NLP have made large-scale text analysis more accessible, the analysis of real-world textual data is still complex and requires several consecutive steps. ObjectiveWe developed and subsequently examined the utility and scientific value of an NLP pipeline for extracting real-world experiences from textual data to provide guidance for applied researchers. MethodsWe applied the NLP pipeline to large-scale textual data collected by the Swiss Multiple Sclerosis (MS) registry. Such textual data constitute an ideal use case for the study of real-world text data. Specifically, we examined 639 text reports on the experienced impact of the first COVID-19 lockdown from the perspectives of persons with MS. The pipeline has been implemented in Python and complemented by analyses of the “Linguistic Inquiry and Word Count” software. It consists of the following 5 interconnected analysis steps: (1) text preprocessing; (2) sentiment analysis; (3) descriptive text analysis; (4) unsupervised learning–topic modeling; and (5) results interpretation and validation. ResultsA topic modeling analysis identified the following 4 distinct groups based on the topics participants were mainly concerned with: “contacts/communication;” “social environment;” “work;” and “errands/daily routines.” Notably, the sentiment analysis revealed that the “contacts/communication” group was characterized by a pronounced negative emotional tone underlying the text reports. This observed heterogeneity in emotional tonality underlying the reported experiences of the first COVID-19–related lockdown is likely to reflect differences in emotional burden, individual circumstances, and ways of coping with the pandemic, which is in line with previous research on this matter. ConclusionsThis study illustrates the timely and efficient applicability of an NLP pipeline and thereby serves as a precedent for applied researchers. Our study thereby contributes to both the dissemination of NLP techniques in applied health sciences and the identification of previously unknown experiences and burdens of persons with MS during the pandemic, which may be relevant for future treatment.

Background: Evidence on mortality risks associated with MS-immunotherapies during the SARS-CoV2 pandemic derived thus far mainly from single country experiences. Objective: In this analysis, we aim to determine the frequency of COVID-19 associated fatality reports of patients receiving an MS-immunotherapy as reported to the international Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) from February 2020 to March 2021. Methods: In all, 1071 cases for this cross-sectional analysis were retrieved from FAERS and a multivariable logistic regression was performed. We adjusted for sex, age, region, month of report to FDA, immunotherapy-class and additionally for healthcare-system and pandemic-related metrics. Result: Anti-CD20 therapies (60%) followed by sphingosine-1 phosphate modulators (12%) and dimethylfumarat (10%) were reported most frequently. In 50% of the cases, MS-phenotype is not reported, relapsing MS in 35% and progressive MS in 15%. Besides older age (odds ratio [OR]: 1.1; 95% confidence interval [CI]: 1.07–1.13; p

Abstract Background Spectral‐domain optical coherence tomography (OCT) may detect retinal changes as a biomarker in neurodegenerative diseases like manifest Huntington's disease (HD). We investigate macular retinal layer thicknesses in a premanifest HD (pre‐HD) cohort and healthy controls (HC). Methods Pre‐HD mutation carriers underwent standardized ratings and a preset macular OCT scan. Thickness values were determined for each sector of all macular retinal layers, the mean of all sectors and the mean of the inner ring (IR, 3 mm) after segmentation (Heyex segmentation batch). HC were retrospectively included from an existing database. The IR thickness of the ganglion cell layer (GCL), retinal nerve fiber layer (RNFL), GCL + inner plexiform layer (GCIPL), and total retina were included in the exploratory correlation analyses with paraclinical ratings and compared to HC. Results The analyses comprised n = 24 pre‐HD participants (n = 10 male, n = 14 female) and n = 38 HC (n = 14 male, n = 24 female). Retinal layer parameters did not correlate with paraclinical ratings. Expected correlations between established HD biomarkers were robust. The IR thicknesses of the GCL, GCIPL, and total retina did not differ between pre‐HD and HC. The IR thickness of the RNFL was significantly higher in pre‐HD participants (pre‐HD: 23.22 μm (standard deviation 2.91), HC: 21.26 μm (1.90), p = .002). Discussion In this cross‐sectional cohort of genetically determined pre‐HD participants, neurodegenerative features were not detected with retinal layer segmentation. Since our pre‐HD collective was more than 16 years before disease onset, OCT may not be sensitive enough to detect early changes.

Abstract Segmentation of white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. In this paper we explore segmentation solutions based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of lesions and grey-matter structures in multi-modal MR imaging, and the performance of these methods when applied to out-of-centre data. We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN). We then retrained those methods on a larger dataset from a single centre, with and without labels for other brain structures. We quantified changes in performance owing to dataset shift, and changes in performance by adding the additional brain-structure labels. We also compared performance with freely available reference methods. Both fully-convolutional CNN methods substantially outperform other approaches in the literature when trained and evaluated in cross-validation on the MSSEG dataset, showing agreement with human raters in the range of human inter-rater variability. Both architectures showed drops in performance when trained on single-centre data and tested on the MSSEG dataset. When trained with the addition of weak anatomical labels derived from Freesurfer, the performance of the 3D Unet degraded, while the performance of the DeepSCAN net improved. Overall, the DeepSCAN network predicting both lesion and anatomical labels was the best-performing network examined.

Introduction Post-COVID-19 syndrome affects approximately 10-25% of people suffering from COVID-19 infection, irrespective of initial COVID-19 severity. Fatigue is one of the major symptoms, occurring in 30-90% of people with post-COVID-19 syndrome. This study aims at describing factors associated with fatigue in people with Post-COVID-19 seen in our newly established Post-Covid clinic. Methods This retrospective single center study included 42 consecutive patients suffering from Post-COVID-19 syndrome treated at the Department of Neurology, University Hospital Bern, between 11/2020 and05/2021. Clinical phenomenology of Post-COVID-19 syndrome with a special focus on fatigue and risk factor identification was performed using Mann-Whitney U Test, Pearson Correlation, and Chi-Quadrat-Test. Results Fatigue (90.5%) was the most prevalent Post-COVID-19 symptom followed by depressive mood (52.4%) and sleep disturbance (47.6%). Fatigue was in mean severe (Fatigue severity scale (FSS) mean 5.5 points (95% Confidence interval (95CI) 5.1 - 5.9, range .9 - 6.9, n = 40), and it was unrelated to age, COVID-19 severity or sex. The only related factors with fatigue severity were daytime sleepiness and depressed mood. Conclusion Fatigue is the main symptom of the Post-COVID-19 syndrome in our cohort. Further studies describing this syndrome are needed to prepare the healthcare systems for the challenge of treating patients with Post-COVID-19 syndrome.

Abstract Background The multi-drug resistance transporter ABCG2, a member of the ATP-binding cassette (ABC) transporter family, mediates the efflux of different immunotherapeutics used in multiple sclerosis (MS), e.g., teriflunomide (teri), cladribine, and mitoxantrone, across cell membranes and organelles. Hence, the modulation of ABCG2 activity could have potential therapeutic implications in MS. In this study, we aimed at investigating the functional impact of abcg2 modulation on teri-induced effects in vitro and in vivo. Methods T cells from C57BL/6 J wild-type (wt) and abcg2-knockout (KO) mice were treated with teri at different concentrations with/without specific abcg2-inhibitors (Ko143; Fumitremorgin C) and analyzed for intracellular teri concentration (HPLC; LS-MS/MS), T cell apoptosis (annexin V/PI), and proliferation (CSFE). Experimental autoimmune encephalomyelitis (EAE) was induced in C57BL/6J by active immunization with MOG35–55/CFA. Teri (10 mg/kg body weight) was given orally once daily after individual disease onset. abcg2-mRNA expression (spinal cord, splenic T cells) was analyzed using qRT-PCR. Results In vitro, intracellular teri concentration in T cells was 2.5-fold higher in abcg2-KO mice than in wt mice. Teri-induced inhibition of T cell proliferation was two fold increased in abcg2-KO cells compared to wt cells. T cell apoptosis demonstrated analogous results with 3.1-fold increased apoptosis after pharmacological abcg2-inhibition in wt cells. abcg2-mRNA was differentially regulated during different phases of EAE within the central nervous system and peripheral organs. In vivo, at a dosage not efficacious in wt animals, teri treatment ameliorated clinical EAE in abcg2-KO mice which was accompanied by higher spinal cord tissue concentrations of teri. Conclusion Functional relevance of abcg2 modulation on teri effects in vitro and in vivo warrants further investigation as a potential determinant of interindividual treatment response in MS, with potential implications for other immunotherapies.

Abstract Background The polysaccharide capsule is a major virulence factor of S. pneumoniae in diseases such as meningitis. While some capsular serotypes are more often found in invasive disease, high case fatality rates are associated with those serotypes more commonly found in asymptomatic colonization. We tested whether growth patterns and capsule size in human cerebrospinal fluid depends on serotype using a clinical isolate of S. pneumoniae and its capsule switch mutants. Results We found that the growth pattern differed markedly from that in culture medium by lacking the exponential and lysis phases. Growth in human cerebrospinal fluid was reduced when strains lost their capsules. When a capsule was present, growth was serotype-specific: high carriage serotypes (6B, 9 V, 19F and 23F) grew better than low carriage serotypes (7F, 14, 15B/C and 18C). Growth correlated with the case-fatality rates of serotypes reported in the literature. Capsule size in human cerebrospinal fluid also depended on serotype. Conclusions We propose that serotype-specific differences in disease severity observed in meningitis patients may, at least in part, be explained by differences in growth and capsule size in human cerebrospinal fluid. This information could be useful to guide future vaccine design.

BackgroundHigh-resolution optical coherence tomography (OCT) allows the detection of macular pathology and involvement of the optic nerve in a wide spectrum of diseases. For the differentiation of diseased and healthy status, normal values of retinal layer segmentation are critical. Yet, normative values mostly cover adult populations with only sparse data for paediatric cohorts. We present data of retinal layer characteristics via OCT in a healthy paediatric cohort.MethodsThis prospective cross-sectional study screened 75 healthy children (male = 42, female = 33, range 4-17 years) without visual problems. OCT was performed with a peripapillary ring and macula scan protocol to determine paediatric normative values for routine parameters (peripapillary retinal nerve fibre layer thickness (pRNFL), total macular volume (TMV), macular retinal thickness (RT)). The macula scan (6mm grid) was segmented using the device-inherent automated segmentation software (Heidelberg Eye Explorer) for retinal layers: RNFL, ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL) in 9 segments each and mean of the 9 segments.ResultsWe obtained OCT data of 72 children with mean age 12.49 years (standard deviation, SD, 2.18; minimum 3.93). Mean global pRNFL was 102.20 μm (SD 8.24), mean TMV 8.81 mm3 (0.30) and mean RT (all segments) 318.22 μm (10.19). Segmented macular retinal layer thicknesses (mean of all segments) were: RNFL 27.67 μm (2.14), GCL 41.94 μm (2.50), IPL 34.97 μm (2.10), INL 35.18 μm (2.15), OPL 29.06 μm (2.24), ONL 68.35 μm (6.20).ConclusionThe OCT is a useful non-invasive imaging technique for the examination of the retina in children with short duration, high imaging resolution and no known adverse effects. Normative values may serve as a comparator for different neuropaediatric disorders and are first presented with this study using an up-to-date and standardized OCT imaging technique.

Multiple sclerosis (MS), particularly relapsing MS (RMS), has become a treatable disease in recent decades, and immunotherapies are now able to influence long-term disease course. A wide range of disease-modifying drugs are available, which makes the choice of therapy in individual cases considerably more complex. Due to specific regulatory aspects (partly diverging approvals by Swissmedic compared to the European Medicines Agency (EMA), and an independent evaluation process for the Federal Office of Public Health (FOPH) specialities list (SL)), we issued a consensus recommendation regarding specific aspects of immunotherapy for MS in Switzerland in 2019. Here, we present revised recommendations with an update on newly approved drugs and new safety aspects, also in reference to the risk of COVID-19 infection and vaccination.

Aims: To retrospectively analyse the Bernese radiologically isolated syndrome (RIS) cohort with the goal of developing a prediction score for conversion to multiple sclerosis (MS). Methods: A total of 31 patients with RIS were identified by screening medical records of neurological patients seen at the University Hospital of Bern between 2004 and 2017 for the diagnoses ‘radiologically isolated syndrome’ and ‘RIS’ adhering to 2009 Okuda recommendations. We analysed clinical, paraclinical and magnetic resonance imaging data during a maximum follow-up period of 3 years and identified significant predictors of conversion to MS. Results: Data were available for 31 patients meeting 2009 Okuda RIS criteria. During the 3 years of follow up, 5/31 RIS patients converted to relapsing-remitting (RR) MS. In our univariate analysis, gadolinium (Gd) enhancement, brainstem and cerebellar hemisphere lesions, immune cell count and albumin concentration in cerebrospinal fluid (CSF), and anti-nuclear antibody (ANA) positivity in serum were identified as significant predictors of conversion to MS. Integrating these factors into our ‘RIS–MS prediction score’ enabled us to calculate a cut-off for prediction of conversion to MS within 3 years with high specificity [1.0, 95% confidence interval (CI) 0.84–1.00) and acceptable sensitivity (0.6, 95% CI 0.17–0.93)]. Conclusion: Our RIS–MS prediction score, if validated in an independent cohort, integrating radiological (Gd enhancement, brainstem and cerebellar hemisphere lesions) and paraclinical factors (ANA in serum, cell count and albumin in CSF) could be a useful prognostic tool for early recognition of RIS patients with a high risk of clinical progression to MS.

Background: Glucocorticoid (GC) pulse therapy is used for multiple sclerosis (MS) relapse treatment; however, GC resistance is a common problem. Considering that GC dosing is individual with several response-influencing factors, establishing a predictive model, which supports clinicians to estimate the maximum GC dose above which no additional therapeutic value can be expected presents a huge clinical need. Method: We established two, independent retrospective cohorts of MS patients. The first was an explorative cohort for model generation, while the second was established for its validation. Using the explorative cohort, a multivariate regression analysis with the GC dose used as the dependent variable and serum vitamin D (25D) concentration, sex, age, EDSS, contrast enhancement on cranial magnetic resonance imaging (MRI), immune therapy, and the involvement of the optic nerve as independent variables was established. Results: In the explorative cohort, 113 MS patients were included. 25-hydroxyvitamin D (25D) serum concentration and the presence of optic neuritis were independent predictors of the GC dose needed to treat MS relapses [(25D): −25.95 (95% confidence interval (CI)): −47.40 to −4.49; p = 0.018; optic neuritis: 2040.51 (95% CI: 584.64–3496.36), p = 0.006]. Validation of the multivariate linear regression model was performed within a second cohort. Here, the predicted GC dose did not differ significantly from the dose administered in clinical routine (mean difference: −843.54; 95% CI: −2078.08–391.00; n = 30, p = 0.173). Conclusion: Our model could predict the GC dose given in clinical, routine MS relapse care, above which clinicians estimate no further benefit. Further studies should validate and improve our algorithm to help the implementation of predictive models in GC dosing.

Objective: Anti-CD20-treated patients are at risk of a reduced humoral immune response during the SARS-CoV-2 pandemic. Our aim was to compare the antibody response after two vaccinations with the mRNA vaccines BNT162b2 or mRNA-1273 in patients with multiple sclerosis. Methods: Data from the University Hospital of Bern and Cantonal Hospital of Lucerne were retrospectively collected from medical records and then analyzed. Anti-spike IgG serum titers were collected from both centers and were considered to be protective from a value of ≥100 AU/mL. Continuous variables were given as the mean and 95% confidence interval (95% CI); categorical variables were given as frequencies. A Mann–Whitney test and Fisher’s exact test as well as a multivariable linear regression analysis with anti-spike IgG (AU/mL) as the dependent variable were run using SPSS Statistic 25 (IBM Corp., Amonk, NY, USA). Results: A total of 74 patients were included; 41/74 (63.51%) were female patients and the mean age was 46.6 years (95% CI 43.4–49.9). Of these patients, 36/74 were vaccinated with BNT162b2 and 38/74 with mRNA-1273, following the national vaccination recommendation. In both vaccine groups, protective anti-spike IgG titers (≥100 AU/mL) were infrequently achieved (5/74: mRNA-1273 3/38; BNT162b2 2/36). Conclusions: In addition to a low rate of protective anti-spike IgG titers in both vaccine groups, we identified a drop in anti-spike IgG serum titers over time. This observation bears therapeutic consequences, as initial positive titers should be checked in case of an infection with the SARS-CoV-2 virus to identify patients who would benefit from an intravenous anti-spike IgG treatment against acute COVID-19.

Background: Blood–brain barrier dysfunction in active multiple sclerosis (MS) lesions leads to pathological changes of cerebrospinal fluid (CSF). Theoretically, CSF analyses could help to predict relapse recovery and the course of disability. In this monocentric study, we investigated the impact of CSF findings assessed during the first relapse of MS on the short-term course of disability. Methods: We performed a retrospective observational study including MS patients with available CSF data after onset of first MS relapse. Clinical symptoms had to be accompanied by gadolinium-enhanced lesion on magnetic resonance imaging. Expanded Disability Status Scale (EDSS) assessments at timepoint of relapse and after relapse recovery were studied to analyze disability. A two-step multivariate linear regression analysis adjusted for EDSS at spinal tab, duration of symptoms, sex, time until post relapse EDSS assessment, immunotherapy post relapse, and relapse treatment with glucocorticoids/plasma exchange to predict relapse associated disability was run. Results: In the first step of the regression model, pathological albumin quotient (QAlb) [regression coefficient 0.50, 95% confidence interval (CI) (0.07–0.92), p = 0.02, n = 99] and CSF protein concentration [regression coefficient 0.84, 95% CI (0.33–1.35), p = 0.001, n = 99] predicted EDSS after relapse recovery. In the second step, the sum score of both predictors [range 0–2; n per value: 0 ( n = 73), 1 ( n = 10), 2 ( n = 15)] confirmed the negative impact on course of disability after relapse [regression coefficient 0.38, 95% CI (0.13–0.62), p = 0.003, n = 98]. In this final multivariate linear regression model ( p

Waning humoral responses to SARS-CoV-2 vaccination have been reported arguing for booster vaccinations even in healthy populations. Multiple sclerosis (MS) immunotherapy with anti-CD20 monoclonal antibodies may negatively influence morbidity and mortality of COVID-19. The opportunity to treat patients at risk for a severe COVID-19 course with specific monoclonal antibodies targeting SARS-CoV-2 represents an important novel measure for patient safety. We report a patient with waning humoral vaccination response around five months after two mRNA vaccination doses upon initiation of ocrelizumab treatment. Symptomatic COVID-19 infection was treated with casirivimab/imdevimab with rapid symptom recovery.

Background: We aim to evaluate serum neurofilament light chain (sNfL), indicating neuroaxonal damage, as a biomarker at diagnosis in a large cohort of early multiple sclerosis (MS) patients. Methods: In a multicentre prospective longitudinal observational cohort, patients with newly diagnosed relapsing-remitting MS (RRMS) or clinically isolated syndrome (CIS) were recruited between August 2010 and November 2015 in 22 centers. Clinical parameters, MRI, and sNfL levels (measured by single molecule array) were assessed at baseline and up to four-year follow-up. Findings: Of 814 patients, 54.7% (445) were diagnosed with RRMS and 45.3% (369) with CIS when applying 2010 McDonald criteria (RRMS[2010] and CIS[2010]). After reclassification of CIS[2010] patients with existing CSF analysis, according to 2017 criteria, sNfL levels were lower in CIS[2017] than RRMS[2017] patients (9.1 pg/ml, IQR 6.2–13.7 pg/ml, n = 45; 10.8 pg/ml, IQR 7.4–20.1 pg/ml, n = 213; p = 0.036). sNfL levels correlated with number of T2 and Gd+ lesions at baseline and future clinical relapses. Patients receiving disease-modifying therapy (DMT) during the first four years had higher baseline sNfL levels than DMT-naïve patients (11.8 pg/ml, IQR 7.5-20.7 pg/ml, n = 726; 9.7 pg/ml, IQR 6.4–15.3 pg/ml, n = 88). Therapy escalation decisions within this period were reflected by longitudinal changes in sNfL levels. Interpretation: Assessment of sNfL increases diagnostic accuracy, is associated with disease course prognosis and may, particularly when measured longitudinally, facilitate therapeutic decisions. Funding: Supported the German Federal Ministry for Education and Research, the German Research Council, and Hertie-Stiftung.

Background: Multiple sclerosis (MS) is the most common chronic, non-traumatic, neurologic disease in young adults. While approximate values of the disease burden of MS are known, individual drivers are unknown.Objective: To estimate the age-, sex-, and disease severity-specific contributions to the disease burden of MS.Methods: We estimated the disease burden of MS using disability-adjusted life years (DALYs) following the Global Burden of Disease study (GBD) methodology. The data sources consisted of the Swiss MS Registry, a recent prevalence estimation, and the Swiss mortality registry.Results: The disease burden of MS in Switzerland in 2016 was 6,938 DALYs (95%-interval: 6,018-7,955), which corresponds to 97 DALYs per 100,000 adult inhabitants. Morbidity contributed 59% of the disease burden. While persons in an asymptomatic (EDSS-proxy 0) and mild (EDSS-proxy >0–3.5) disease stage represent 68.4% of the population, they make up 39.8% of the MS-specific morbidity. The remaining 60.2% of the MS-specific morbidity stems from the 31.6% of persons in a moderate (EDSS-proxy 4–6.5) or severe (EDSS-proxy ≥7) disease stage.Conclusions: Morbidity has a larger influence on the disease burden of MS than mortality and is shared in a ratio of 2:3 between persons in an asymptomatic/mild and moderate/severe disease stage in Switzerland. Interventions to reduce severity worsening in combination with tailored, symptomatic treatments are important future paths to lower the disease burden of MS.

The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of ‘new or enlarged’ is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter- and intra-rater variability. Automated methods for lesion quantification, if accurate enough, hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separate radiologically progressive from radiologically stable patients, despite this being a pressing clinical use-case. In this paper, we explore the ability of a deep learning segmentation classifier to separate stable from progressive patients by lesion volume and lesion count, and find that neither measure provides a good separation. Instead, we propose a method for identifying lesion changes of high certainty, and establish on an internal dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable time-points with a very high level of discrimination (AUC = 0.999), while changes in lesion volume are much less able to perform this separation (AUC = 0.71). Validation of the method on two external datasets confirms that the method is able to generalize beyond the setting in which it was trained, achieving an accuracies of 75 % and 85 % in separating stable and progressive time-points. Keywords: Deep Learning, Multiple Sclerosis, MRI, Longitudinal Imaging

The COVID-19 pandemic specifically affects the management and treatment of patients with autoimmune neurological disorders. Major concerns include potentially higher risks of infection or severe disease course under certain immunotherapies used to treat those disorders and the influence of COVID-19 on the underlying disease. We present data of the neuroimmunological outpatient department of the University Hospital of Bern (Switzerland). 24 cases were analyzed, 19 of them suffered from Multiple Sclerosis. Of these 24 patients, 6 were hospitalized, 2/6 were treated in the Intensive Care Unit. Possible risk factors for severe course (defined as need for hospitalization) observed in our cohort included cardiovascular risk factors, treatment with B-cell depleting agents, Sphingosine-1 Phosphate Receptor Modulators, and oral steroid therapies. These data are based on a small, retrospective observational cohort and should be interpreted with caution, although they are in line with several other cohort studies.

The interrelations between fatigue, depression and health-related quality of life (HRQoL) in persons with multiple sclerosis (PwMS) are complex, and the directionality of the effects is unclear. To address this gap, the current study used a longitudinal design to assess direct and indirect effects of fatigue and depression on HRQoL in a one-year follow-up survey. A sample of 210 PwMS from the nationwide Swiss MS Registry was used. HRQoL was assessed using the European Quality of Life 5-Dimension 5-Level questionnaire. Path analysis on HRQoL, with fatigue and depression as predictors, was applied. Fatigue was measured by the Modified Fatigue Impact Scale (MFIS), including physical, cognitive and psychosocial subscales, and non-somatic depressive symptomatology was examined with the Beck Depression Inventory-Fast Screen (BDI-FS). Fatigue acted as a fully mediating variable (B = −0.718, SE = 0.253) between non-somatic depressive symptomatology and HRQoL. This indirect effect became apparent in the physical (B = −0.624, SE = 0.250), psychosocial (B = −0.538, SE = 0.256) and cognitive subscales (B = −0.485, SE = 0.192) of fatigue. In contrast, non-somatic depressive symptomatology did not act as a mediator. Our findings provide novel and clinically relevant longitudinal evidence showing that the debilitating effect of non-somatic aspects of depression on HRQoL was fully mediated and therefore explainable via fatigue.

Background: In multiple sclerosis (MS), the frequency of hypogammaglobulinemia is unknown. We aimed to evaluate the frequency of reduced immunoglobulin (Ig) concentrations and its association with immunotherapy and disease course in two independent MS cohorts. Methods: In our retrospective cross-sectional study, MS patients and control patients with head or neck pain from Bern University Hospital (Bern, Switzerland) and Eginition University Hospital (Athens, Greece) were included. The lower limits of normal (LLN) for serum Ig concentration were IgG < 700 mg/dl, IgM < 40 mg/dl, and IgA < 70 mg/dl. Mann–Whitney U test, analysis of variance test, and multiple linear regression analysis were employed. Results: In total, 327 MS patients were retrospectively identified (Bern/Athens: n = 226/101). Serum IgG concentrations were frequently under LLN in both MS cohorts (Bern/Athens: 15.5%/14.9%), even when considering only untreated patients (Bern/Athens: 7.9%/8.6%). MS patients ( n = 327) were significantly more likely to have IgG concentrations below LLN and below 600 mg/dl in comparison with controls ( n = 58) ( p = 0.015 and 0.047, respectively). Between both patient groups, no significant differences were found in frequencies of IgA and IgM concentrations under LLN [ n (MS patients/controls): IgA 203/30, IgM 224/24]. Independently of age, secondary progressive MS patients had lower IgG concentrations than relapsing–remitting and primary progressive patients (both: p ⩽ 0.01). After adjusting for sex, age, and disease course, IgG concentrations were lower in patients treated with rituximab ( p = 0.001; n = 42/327), intravenous corticosteroids ( p

Background: Dimethyl fumarate (DMF) is licensed for treatment of relapsing–remitting multiple sclerosis (RRMS). DMF can induce lymphopenia, which is assumed to increase the risk for opportunistic infections like progressive multifocal leukoencephalopathy. Our goal for this work was to estimate the frequency of grade 3 lymphopenia in DMF-treated patients with RRMS and to characterize patient-sided factors influencing the time course of lymphocyte repopulation after DMF withdrawal. Material and methods: A single-center retrospective data analysis was performed at University Hospital Bern, Switzerland. Patients with DMF treatment were analyzed for lymphocyte counts. Demographic factors were statistically analyzed in grade 3 lymphopenic patients. Results: We estimated a grade 3 lymphopenia frequency of 11/246 (4.5%), corroborating previous studies. In all patients, lymphocytes recovered to values ⩾800/µl within 0.5 years. Multivariate linear regression analysis unmasked older age as being associated with a longer duration of repopulation. Conclusion: Considering the aging population, our findings warrant further investigations of DMF-induced lymphopenia.

c-Jun N-terminal kinase (JNK) is upregulated during multiple sclerosis relapses and at the peak of experimental autoimmune encephalomyelitis (EAE). We aim to investigate the effects of pharmacological pan-JNK inhibition on the course of myelin oligodendrocyte glycoprotein (MOG35-55) EAE disease using in vivo and in vitro experimental models. EAE was induced in female C57BL/6JRj wild type mice using MOG35-55. SP600125 (SP), a reversible adenosine triphosphate competitive pan-JNK inhibitor, was then given orally after disease onset. Positive correlation between SP plasma and brain concentration was observed. Nine, but not three, consecutive days of SP treatment led to a significant dose-dependent decrease of mean cumulative MOG35-55 EAE severity that was associated with increased mRNA expression of interferon gamma (INF-γ) and tumor necrosis factor alpha (TNF-α) in the spinal cord. On a histological level, reduced spinal cord immune cell-infiltration predominantly of CD3+ T cells as well as increased activity of Iba1+ cells were observed in treated animals. In addition, in vitro incubation of murine and human CD3+ T cells with SP resulted in reduced T cell apoptosis and proliferation. In conclusion, our study demonstrates that pharmacological pan-JNK inhibition might be a treatment strategy for autoimmune central nervous system demyelination.

Objective: Differential diagnosis of neuromyelitis optica spectrum disorders (NMOSD) and multiple sclerosis (MS) or mimics can be challenging, especially in patients with atypical presentations and negative serostatus for aquaporin-4 antibodies (AQP4-Ab). This brief research report describes magnetic resonance imaging (MRI) findings focusing on quantitative apparent diffusion coefficient (ADC) histogram analysis as a potential tool to differentiate NMOSD from MS.Methods: Longitudinal MRI data obtained during routine clinical examinations were retrospectively analyzed in a patient with histologically determined cerebral NMOSD, a patient with an acute tumefactive MS lesion, and a patient with ischemic stroke. Histogram analyses of ADC maps were evaluated.Results: A patient diagnosed with MS experienced a severe rebound after fingolimod withdrawal and a single daclizumab injection. Cerebral NMOSD manifestation was confirmed by brain biopsy. However, the patient did not fulfill consensus criteria for NMOSD and was AQP4-Ab negative. Comparison of ADC histogram analyses of this patient with those from a patient with MS and one with ischemic stroke revealed differential ADC characteristics: namely a more pronounced and prolonged ADC leftward shift in inflammatory than in ischemic pathology, even more accentuated in NMOSD versus MS.Conclusion: ADC map histograms and ADC threshold values for different conditions may be useful for differentiation of large inflammatory brain lesions and further studies are merited.

IntroductionVolumetric image analysis to detect progressive brain tissue loss in patients with multiple sclerosis (MS) has recently been suggested as a promising marker for “no evidence of disease activity.” Software packages for longitudinal whole-brain volume analysis in individual patients are already in clinical use; however, most of these methods have omitted region-based analysis. Here, we suggest a fully automatic analysis pipeline based on the free software packages FSL and FreeSurfer.Materials and methodsFifty-five T1-weighted magnetic resonance imaging (MRI) datasets of five patients with confirmed relapsing–remitting MS and mild to moderate disability were longitudinally analyzed compared to a morphometric reference database of 323 healthy controls (HCs). After lesion filling, the volumes of brain segmentations and morphometric parameters of cortical parcellations were automatically screened for global and regional abnormalities. Error margins and artifact probabilities of regional morphometric parameters were estimated. Linear models were fitted to the series of follow-up MRIs and checked for consistency with cross-sectional aging in HCs.ResultsAs compared to leave-one-out cross-validation in a subset of the control dataset, anomaly detection rates were highly elevated in MRIs of two patients. We detected progressive volume changes that were stronger than expected compared to normal aging in 4/5 patients. In individual patients, we also identified stronger than expected regional decreases of subcortical gray matter, of cortical thickness, and areas of reducing gray–white contrast over time.ConclusionStatistical comparison with a large normative database may provide complementary and rater independent quantitative information about regional morphological changes related to disease progression or drug-related disease modification in individual patients. Regional volume loss may also be detected in clinically stable patients.