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Background: Cortical lesions (CLs) have been reported to be a better predictor for cognitive impairment than white matter (WM) lesions in relapsing–remitting multiple sclerosis (RRMS). Objectives: The objectives of this article are to investigate the contribution of CLs and WM lesions to cognitive impairment in 91 patients with MS and clinically isolated syndrome, and to test potential associations of CLs and WM lesions with fatigue and depression. Methods: Lesions were scored and segmented on 3D double inversion recovery sequences, according to their location (cortical, WM). Normalised grey matter volume was also determined. Cognitive performance was assessed with the SDMT and PASAT-3, fatigue with the FSMC and depression with the German version of the CES-D. Results: CL volume did not correlate with fatigue or depression, but correlated significantly with both neuropsychological outcome measures: PASAT-3 (r = −0.275, p = 0.009) and SDMT (r = −0.377, p < 0.001). Multiple regression analyses with age, WM lesions, CLs and GM volume as independent variables, however, did not reveal CL volume as a significant predictor of neuropsychological outcomes, whereas WM lesion volume significantly predicted SDMT and by trend PASAT performance. Conclusions: These findings suggest a role of WM lesions in the development of cognitive deficits, especially information-processing speed, which may be higher than previously assumed. Abbreviations: CES-D: Center for Epidemiologic Studies Depression scale (ADS-L: Allgemeine Depressions Skala-L, German version of CES-D), CIS: clinically isolated syndrome, CL: cortical lesion, DIR: double inversion recovery, EDSS: Expanded Disability Status Scale, FSMC: fatigue scale for motor and cognitive functions, GM: grey matter, MRI: magnetic resonance imaging, MS: multiple sclerosis, PASAT-3: paced auditory serial addition test 3s, PPMS: primary progressive multiple sclerosis, RRMS: relapsing–remitting multiple sclerosis, SDMT: symbol digit modalities test, SPM: statistical parametric mapping, SPMS: secondary progressive multiple sclerosis, WM: white matter

Background: Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. MS lesions show a typical distribution pattern and primarily affect the white matter (WM) in the periventricular zone and in the centrum semiovale. Objective: To track lesion development during disease progression, we compared the spatiotemporal distribution patterns of lesions in relapsing–remitting MS (RRMS) and secondary progressive MS (SPMS). Methods: We used T1 and T2 weighted MR images of 209 RRMS and 62 SPMS patients acquired on two different 1.5 Tesla MR scanners in two clinical centers followed up for 25 (± 1.7) months. Both cross-sectional and longitudinal differences in lesion distribution between RRMS and SPMS patients were analyzed with lesion probability maps (LPMs) and permutation-based inference. Results: MS lesions clustered around the lateral ventricles and in the centrum semiovale. Cross-sectionally, compared to RRMS patients, the SPMS patients showed a significantly higher regional probability of T1 hypointense lesions (p≤0.03) in the callosal body, the corticospinal tract, and other tracts adjacent to the lateral ventricles, but not of T2 lesions (peak probabilities were RRMS: T1 9%, T2 18%; SPMS: T1 21%, T2 27%). No longitudinal changes of regional T1 and T2 lesion volumes between baseline and follow-up scan were found. Conclusion: The results suggest a particular vulnerability to neurodegeneration during disease progression in a number of WM tracts.

Neuroanatomical pattern classification using support vector machines (SVMs) has shown promising results in classifying Multiple Sclerosis (MS) patients based on individual structural magnetic resonance images (MRI). To determine whether pattern classification using SVMs facilitates predicting conversion to clinically definite multiple sclerosis (CDMS) from clinically isolated syndrome (CIS). We used baseline MRI data from 364 patients with CIS, randomised to interferon beta-1b or placebo. Non-linear SVMs and 10-fold cross-validation were applied to predict converters/non-converters (175/189) at two years follow-up based on clinical and demographic data, lesion-specific quantitative geometric features and grey-matter-to-whole-brain volume ratios. We applied linear SVM analysis and leave-one-out cross-validation to subgroups of converters (n = 25) and non-converters (n = 44) based on cortical grey matter segmentations. Highest prediction accuracies of 70.4% (p = 8e-5) were reached with a combination of lesion-specific geometric (image-based) and demographic/clinical features. Cortical grey matter was informative for the placebo group (acc.: 64.6%, p = 0.002) but not for the interferon group. Classification based on demographic/clinical covariates only resulted in an accuracy of 56% (p = 0.05). Overall, lesion geometry was more informative in the interferon group, EDSS and sex were more important for the placebo cohort. Alongside standard demographic and clinical measures, both lesion geometry and grey matter based information can aid prediction of conversion to CDMS.

Univariate analyses have identified gray matter (GM) alterations in different groups of MS patients. While these methods detect differences on the basis of the single voxel or cluster, multivariate methods like support vector machines (SVM) identify the complex neuroanatomical patterns of GM differences. Using multivariate linear SVM analysis and leave-one-out cross-validation, we aimed at identifying neuroanatomical GM patterns relevant for individual classification of MS patients. We used SVM to separate GM segmentations of T1-weighted three-dimensional magnetic resonance (MR) imaging scans within different age- and sex-matched groups of MS patients with either early (n=17) or late MS (n=17) (contrast I), low (n=20) or high (n=20) white matter lesion load (contrast II), and benign MS (BMS, n=13) or non-benign MS (NBMS, n=13) (contrast III) scanned on a single 1.5T MR scanner. GM patterns most relevant for individual separation of MS patients comprised cortical areas of all the cerebral lobes as well as deep GM structures, including the thalamus and caudate. The patterns detected were sufficiently informative to separate individuals of the respective groups with high sensitivity and specificity in 85% (contrast I), 83% (contrast II) and 77% (contrast III) of cases. The study demonstrates that neuroanatomical spatial patterns of GM segmentations contain information sufficient for correct classification of MS patients at the single case level, thus making multivariate SVM analysis a promising clinical application. ► First study using multivariate image analysis for classification of grey matter in MS. ► Up to 92% sensitivity and 90% classification accuracy in individual's unseen data. ► Advantage of multivariate analysis in allowing inferences at the individual’s level.

Different pathological processes like demyelination and axonal loss can alter the magnetisation transfer ratio (MTR) in brain tissue. The standard method to measure this effect is to scan the respective tissue twice, one with and one without a specific saturation pulse. A major drawback of this technique based on spoiled gradient echo (GRE) sequences relates to its long acquisition time due to the saturation pulses. Recently, an alternative concept for MT imaging based on balanced steady state free precession (bSSFP) has been proposed. Modification of the duration of the radiofrequency pulses for imaging allows scanning MT sensitive and non-sensitive images. The steady-state character of bSSFP with high intrinsic signal-to-noise ratio (SNR) allows three-dimensional (3D) whole brain MTR at high spatial resolution within short and thus clinically feasible acquisition times. In the present study, both bSSFP-MT and 2D GRE-MT imaging were used in a cohort of 31 patients with multiple sclerosis (MS) to characterize different normal appearing (NA) and pathological brain structures. Under the constraint of identical SNR and scan time, a 3.4 times higher voxel size could be achieved with bSSFP. This increased resolution allowed a more accurate delineation of the different brain structures, especially of cortex, hippocampus and MS lesions. In a multiple linear regression model, we found an association between MTR of cortical lesions and a clinical measure of disability (r= −0.407, p=0.035) in the bSSFP dataset only. The different relaxation weighting of the base images (T2/T1 in bSSFP, proton density in GRE) had no effects besides a larger spreading of the MTR values of the different NA structures. This was demonstrated by the nearly perfect linearity between the NA matter MTR of both techniques as well as in the absolute MTR differences between NA matter and the respective lesions. •We examined 31 MS patients with two magnetization transfer ratio (MTR) techniques.•balanced steady state free precession (bSSFP) was compared with gradient echo (GRE).•Using bSSFP, we gained 3.4 higher spatial resolution compared to standard GRE.•We found nearly perfect linearity between the MTR values of both techniques.•bSSFP MTR maps demonstrate improved correlation to clinical outcomes.

Previous studies have established regional gray matter (GM) volume loss in multiple sclerosis (MS) but the relationship between development of white matter (WM) lesions and changes of regional GM volumes is unclear. The present study addresses this issue by means of voxel-based morphometry (VBM). T1-weighted three-dimensional magnetic resonance imaging (MRI) data from MS patients followed up for 12 months were analyzed using VBM. An analysis of covariance model assessed with cluster size inference (all corrected for multiple comparisons, p < 0.01) was used to compare GM volumes between baseline and follow-up while controlling for age, gender, and disease duration. Lesion burden, i.e. volumes of T1 hypointense and T2 hyperintense lesions and the number of new T2 lesions at year one, was also determined. Comparing all MS patients ( n = 211) longitudinally, GM volume remained unchanged during one year-follow-up. Focusing on patients with relapsing remitting MS (RRMS) ( n = 151), significant cortical GM volume reductions between baseline and follow-up scans were found in the anterior and posterior cingulate, the temporal cortex, and cerebellum. Within the RRMS group, those patients with increasing T2 and T1 lesion burden ( n = 45) showed additional GM volume loss during follow-up in the frontal and parietal cortex, and precuneus. In contrast, patients lacking an increase in WM lesion burden ( n = 44) did not show any significant GM changes. The present study suggests that the progression of regional GM volume reductions is associated with WM lesion progression and occurs predominantly in fronto-temporal cortical areas.

Background: In multiple sclerosis (MS) regional grey matter (GM) atrophy has been associated with disability progression. Objective: The aim of this study was to compare regional GM volume changes in relapsing–remitting MS (RRMS) patients with progressive and stable disability, using voxel-based morphometry (VBM). Methods: We acquired baseline and 1-year follow-up 3-dimensional (3D) T1-weighted magnetic resonance imaging (MRI) data of RRMS patients, using two 1.5-Tesla scanners. Patients were matched pair-wise with respect to age, gender, disease duration, medication, scanner and baseline Expanded Disability Status Scale (EDSS) into 13 pairs, with either progressive EDSS (≥ 1 point change y-1) or stable EDSS, as well as into 29 pairs with either progressive Multiple Sclerosis Functional Composite (MSFC) at ≥ 0.25% decrease in y-1 in any component, or stable MSFC. We analysed longitudinal regional differences in GM volumes in the progressive and stable EDSS and MSFC groups, respectively, using VBM. Results: Significant GM volume reductions occurred in the right precuneus, in the progressive EDSS group. Differential between-group effects occurred in the right precuneus and in the postcentral gyrus. Further longitudinal GM volume reductions occurred in the right orbicular gyrus, in the progressive MSFC group, but no between-group differences were observed (non-stationary cluster-wise inference, all Pcorrected < 0.05). Conclusion: These results suggested a direct association of disability progression and regional GM atrophy in RRMS.

Background Sleep impairment is common in rheumatoid arthritis (RA) but has not been extensively studied with wearables and self-observation following treatment. This study investigates the impact of upadacitinib (UPA) on subjective (self-reported) and objective (actigraphy-based) sleep quality in RA patients. Methods SLEERA is a sub-study of UPHOLD, an international observational cohort study (NCT04497597) that assessed sleep quality in RA patients after UPA treatment. Subjective sleep quality was measured using Pittsburgh Sleep Quality Index (PSQI) at baseline and post-treatment months 3, 6, 12 and 24. Objective sleep quality was assessed by actigraphy at baseline and post-treatment month 3. Results Among 39 patients, 76% had impaired subjective sleep quality at baseline (total PSQI: 7.84). Actigraphy sleep efficiency was 85.1%, 85.6% and 82.1% in patients with good (PSQI ≤ 5), moderate (PSQI > 5 and ≤ 8) and poor (PSQI > 8) sleep quality. After 3 months of UPA treatment, the total PSQI significantly improved (-2.42, p  < 0.001), while actigraphy outcomes remained unchanged across the whole cohort. Of 19 patients with impaired sleep quality at baseline, 8 achieved good sleep quality, accompanied by improvements in actigraphy-based sleep efficiency (+ 1.5%), total wake time (-7.3 min) and movement index (-0.7%). Shifts from objectively impaired to good sleep efficiency were only observed in patients achieving remission or normative values of pain and fatigue. Conclusions Sleep impairment in RA patients can improve with treatment. Actigraphy showed improvements, particularly in patients with high subjective sleep impairment who achieve remission. Trial registration NCT04497597.

Plants are essential for life and are extremely diverse organisms with unique molecular capabilities 1 . Here we present a quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana . Our analysis provides initial answers to how many genes exist as proteins (more than 18,000), where they are expressed, in which approximate quantities (a dynamic range of more than six orders of magnitude) and to what extent they are phosphorylated (over 43,000 sites). We present examples of how the data may be used, such as to discover proteins that are translated from short open-reading frames, to uncover sequence motifs that are involved in the regulation of protein production, and to identify tissue-specific protein complexes or phosphorylation-mediated signalling events. Interactive access to this resource for the plant community is provided by the ProteomicsDB and ATHENA databases, which include powerful bioinformatics tools to explore and characterize Arabidopsis proteins, their modifications and interactions. A quantitative atlas of the transcriptomes, proteomes and phosphoproteomes of 30 tissues of the model plant Arabidopsis thaliana provides a valuable resource for plant research.

Background Following identification of widespread Mycobacterium chimaera infections in 2013 associated with aerosolization from Sorin heater-cooler-devices (HCD), microbiological monitoring of HCDs has become mandatory in Switzerland. Methods Since 01/2016, four Maquet HCU40 (Getinge) HCDs have been in operation at the University Hospital Basel. Monthly water cultures for Gram-negative bacteria and mycobacteria were performed, including samples from two filling lines equipped with 0.2 μm water filters. Intermittent monthly air sampling for mycobacteria was conducted. Results Between 01/2016 and 05/2018, various non-fermenting bacteria were cultured. Mycobacterium chelonae was first isolated in 06/2018 from HCD water tanks. Mycobacteria cultures from the filling lines remained negative. The first air sample positive for M. chelonae was collected in 12/2019. Despite intensified disinfection measures from 01/2022 onwards, M. chelonae could be cultured in air samples, collected at 30 cm and 120 cm height adjacent to three of the four running HCDs in 07/2022. Whole genome sequencing identified a cluster of isolates differing by a maximum of four single nucleotide polymorphisms. Despite extensive investigations, the source of M. chelonae remains undetermined. To date, no infections with M. chelonae have been related to the contaminated HCDs at our institution. Conclusions Our data confirms the risk of aerosolization of non-tuberculous mycobacteria from HCDs, irrespective of HCD-design and manufacturer. These findings underscore the importance of routine microbiological surveillance, including both water and air sampling when HCDs are placed within the operation room to prevent exposure of the operating field to the air exhausts of HCDs.