Multiple sclerosis (MS) is an autoimmune pathology leading to neurodegeneration. Because of the complexity and heterogenic etiology of this disease, diagnosis and treatment for individual patients are challenging. Exosome-associated microRNAs (miRNAs) have recently emerged as a new class of diagnostic biomarkers involved in both autoimmune and neurologic disorders. Interesting new evidence has emerged showing that circulating miRNAs are dysregulated in MS body fluids, including serum, plasma, and cerebrospinal fluid. We hypothesized that exosome-associated miRNAs could present a readily accessible blood-based assay for MS disease. We detected expression of miRNAs by quantitative PCR on a small cohort of MS patients. We analyzed circulating exosome-associated miRNAs of MS patients before and after therapy and found that 14 exosome-associated miRNAs were significantly down-regulated, while 2 exosome-associated miRNAs were significantly up-regulated in IFN-β-treated relapsing-remitting MS patients with response to therapy compared to those without response. We identified a serum miRNA panel that could be used to monitor the response to IFN-β therapy. Overall, these data suggest that circulating exosome-associated miRNA profiling could represent an easily detectable biomarker of disease and treatment response.-Manna, I., Iaccino, E., Dattilo, V., Barone, S., Vecchio, E., Mimmi, S., Filippelli, E., Demonte, G., Polidoro, S., Granata, A., Scannapieco, S., Quinto, I., Valentino, P., Quattrone, A. Exosome-associated miRNA profile as a prognostic tool for therapy response monitoring in multiple sclerosis patients.

This work evaluates the potential in diagnostic application of a new advanced neuroimaging method, which delineates the profile of tissue properties along the corticospinal tract (CST) in amyotrophic lateral sclerosis (ALS), by means of diffusion tensor imaging (DTI). Twenty-four ALS patients and twenty-four demographically matched healthy subjects were enrolled in this study. The Automated Fiber Quantification (AFQ), a tool for the automatic reconstruction of white matter tract profiles, based on a deterministic tractography algorithm to automatically identify the CST and quantify its diffusion properties, was used. At a group level, the highest non-overlapping DTI-related differences were detected in the cerebral peduncle, posterior limb of the internal capsule, and primary motor cortex. Fractional anisotropy (FA) decrease and mean diffusivity (MD) and radial diffusivity (RD) increases were detected when comparing ALS patients to controls. The machine learning approach used to assess the clinical utility of this DTI tool revealed that, by combining all DTI metrics measured along tract between the cerebral peduncle and the corona radiata, a mean 5-fold cross validation accuracy of 80% was reached in discriminating ALS from controls. Our study provides a useful new neuroimaging tool to characterize ALS-related neurodegenerative processes by means of CST profile. We demonstrated that specific microstructural changes in the upper part of the brainstem might be considered as a valid biomarker. With further validations this method has the potential to be considered a promising step toward the diagnostic utility of DTI measures in ALS. Hum Brain Mapp 38:727-739, 2017. © 2016 Wiley Periodicals, Inc.

Machine Learning application on clinical data in order to support diagnosis and prognostic evaluation arouses growing interest in scientific community. However, choice of right algorithm to use was fundamental to perform reliable and robust classification. Our study aimed to explore if different kinds of Machine Learning technique could be effective to support early diagnosis of Multiple Sclerosis and which of them presented best performance in distinguishing Multiple Sclerosis patients from control subjects. We selected following algorithms: Random Forest, Support Vector Machine, Naïve-Bayes, K-nearest-neighbor and Artificial Neural Network. We applied the Independent Component Analysis to resting-state functional-MRI sequence to identify brain networks. We found 15 networks, from which we extracted the mean signals used into classification. We performed feature selection tasks in all algorithms to obtain the most important variables. We showed that best discriminant network between controls and early Multiple Sclerosis, was the sensori-motor I, according to early manifestation of motor/sensorial deficits in Multiple Sclerosis. Moreover, in classification performance, Random Forest and Support Vector Machine showed same 5-fold cross-validation accuracies (85.7%) using only this network, resulting to be best approaches. We believe that these findings could represent encouraging step toward the translation to clinical diagnosis and prognosis.

Background: Rituximab, an anti-CD20 monoclonal antibody leading to B lymphocyte depletion, is increasingly used as an off-label treatment option for multiple sclerosis (MS). Objective: To investigate the effectiveness and safety of rituximab in relapsing–remitting (RR) and progressive MS. Methods: This is a multicenter, retrospective study on consecutive MS patients treated off-label with rituximab in 22 Italian and 1 Swiss MS centers. Relapse rate, time to first relapse, Expanded Disability Status Scale (EDSS) progression, incidence of adverse events, and radiological outcomes from 2009 to 2019 were analyzed. Results: A total of 355/451 enrolled subjects had at least one follow-up visit and were included in the outcome analysis. Annualized relapse rate significantly decreases after rituximab initiation versus the pre-rituximab start year in RRMS (from 0.86 to 0.09, p < .0001) and in secondary-progressive (SP) MS (from 0.34 to 0.06, p < .0001) and had a slight decrease in primary-progressive (PP) MS patients (from 0.12 to 0.07, p = 0.45). After 3 years from rituximab start, the proportion of patients with a confirmed EDSS progression was 14.6% in the RRMS group, 24.7% in the SPMS group, and 41.5% in the PPMS group. No major safety concerns arose. Conclusion: Consistently with other observational studies, our data show effectiveness of rituximab in reducing disease activity in patients with MS.

The main aim of the study is to evaluate the efficacy and safety profile of ocrelizumab (OCR), rituximab (RTX), and cladribine (CLA), employed as natalizumab (NTZ) exit strategies in relapsing–remitting multiple sclerosis (RRMS) patients at high-risk for progressive multifocal leukoencephalopathy (PML). This is a multicentre, retrospective, real-world study on consecutive RRMS patients from eleven tertiary Italian MS centres, who switched from NTZ to OCR, RTX, and CLA from January 1st, 2019, to December 31st, 2019. The primary study outcomes were the annualized relapse rate (ARR) and magnetic resonance imaging (MRI) outcome. Treatment effects were estimated by the inverse probability treatment weighting (IPTW), based on propensity-score (PS) approach. Additional endpoint included confirmed disability progression (CDP) as measured by Expanded Disability Status Scale and adverse events (AEs). Patients satisfying predefined inclusion and exclusion criteria were 120; 64 switched to OCR, 36 to RTX, and 20 to CLA. Patients from the 3 groups did not show differences for baseline characteristics, also after post hoc analysis. The IPTW PS-adjusted models revealed that patients on OCR had a lower risk for ARR than patients on CLA (ExpBOCR 0.485, CI 95% 0.264–0.893, p = 0.020). This result was confirmed also for 12-month MRI activity (ExpBOCR 0.248 CI 95% 0.065–0.948, p = 0.042). No differences were found in other pairwise comparisons (OCR vs RTX and RTX vs CLA) for the investigated outcomes. AEs were similar among the 3 groups. Anti-CD20 drugs were revealed to be effective and safe options as NTZ exit strategies. All investigated DMTs showed a good safety profile.