COVID-19-related and not related Guillain-Barré syndromes share the same management pitfalls during lock down: The experience of Liguria region in Italy.

Recently, during the pandemic infection of the novel SARS-CoV-2, some cases of Guillan-Barré Syndrome (GBS) have been reported. The aim of this work is to report the natural history of patients with GBS, both COVID and not-COVID related, hospitalized in Liguria region, during lock down period, in order to assess clinical features of both groups and possible managements pitfalls due to pandemic emergency. Fifteen GBS patients were admitted to the Hospitals of Liguria, from February 15th to May 3rd 2020, six with SARS-CoV-2 infection and nine without infection. In COVID-19 related GBS five patients presented with classical GBS and one with variant. Two patients presented neurologic symptoms during or shortly after the viral syndrome, suggesting the pattern of a para-infectious profile. Multi-organ involvement, delay in the diagnosis, incomplete work up and start of therapy, were registered in 50% of cases with a GBS-Disability scale ≥4 at follow-up evaluation. In not-COVID-19 related GBS, main problem was diagnostic delay. In three patients the first neurological observation took place after a mean of 33,6 days. Moreover, five patients went to emergency room after an average of 30 days since the onset of neurological symptoms because of fear of contagion. In conclusion, not only SARS-CoV-2 infection can cause GBS, but it can also, due to effects of pandemic on the health organization, affect the outcome of patients with not COVID-19 related GBS.

Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data.

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.

Multimodal assessment of normal-appearing corpus callosum is a useful marker of disability in relapsing-remitting multiple sclerosis: an MRI cluster analysis study.

BACKGROUND AND PURPOSE: Corpus callosum (CC) is frequently involved in relapsing-remitting multiple sclerosis (RRMS). Magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) allow to study CC macrostructural and microstructural tissue integrity. Here, we applied a data-driven approach to MRI and DTI data of normal-appearing CC in RRMS subjects, and subsequently evaluated if differences in tissue integrity corresponded to different levels of physical disability and cognitive impairment. METHODS: 74 RRMS patients and 20 healthy controls (HC) underwent 3 T MRI and DTI. Thickness and fractional anisotropy (FA) along midsagittal CC were extracted, and values from RRMS patients were fed to a hierarchical clustering algorithm. We then used ANOVA to test for differences in clinical and cognitive variables across the imaging-based clusters and HC. RESULTS: We found three distinct MRI-based subgroups of RRMS patients with increasing severity of CC damage. The first subgroup showed callosal integrity similar to HC (Cluster 1); Cluster 2 had milder callosal damage; a third subgroup showed the most severe callosal damage (Cluster 3). Cluster 3 included patients with longer disease duration and worst scores in Expanded Disability Status Scale. Cognitive domains of verbal memory, executive functions and processing speed were impaired in Cluster 3 and Cluster 2 compared to Cluster 1 and HC. CONCLUSIONS: Within the same homogeneous cohort of patients, we could identify three neuroimaging RRMS clusters characterized by different involvement of normal-appearing CC. Interestingly, these corresponded to three distinct levels of clinical and cognitive disability.

Exosome-associated miRNA profile as a prognostic tool for therapy response monitoring in multiple sclerosis patients.

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-ß-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-ß 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.

Hepatic microabscesses during CMV reactivation in a multiple sclerosis patient after alemtuzumab treatment.

The anti-CD52 monoclonal antibody alemtuzumab is a highly active treatment for multiple sclerosis (MS) causing rapid depletion of B and T lymphocytes with nadir one month after last infusion. Opportunistic Cytomegalovirus (CMV) infections have been reported in MS patients treated with this drug. We report one patient who developed a CMV reactivation with hepatic involvement three weeks after the first cycle of alemtuzumab. This patient, promptly diagnosed and treated, achieved a complete recovery with valganciclovir. The possibility of this treatable opportunistic infection should be considered by neurologists in febrile patients with hepatic markers alteration after treatment with alemtuzumab.

Sativex in resistant multiple sclerosis spasticity: Discontinuation study in a large population of Italian patients (SA.FE. study).

BACKGROUND: The approval of Sativex for the management of multiple sclerosis (MS) spasticity opened a new opportunity to many patients. In Italy, the healthcare payer can be fully reimbursed by the involved pharma company with the cost of treatment for patients not responding after a 4 week (28 days) trial period (Payment by Results, PbR), and 50% reimbursed with the cost of 6 weeks (42 days) treatment for other patients discontinuing (Cost Sharing, CS). The aim of our study was to describe the Sativex discontinuation profile from a large population of spasticity treated Italian MS patients. METHODS: We collected data of patients from 30 MS centres across the country starting Sativex between January 2014 and February 2015. Data were collected from the mandatory Italian Medicines Agency (AIFA) web-registry. Predictors of treatment discontinuation were assessed using a multivariate Cox proportional regression analysis. RESULTS: During the observation period 631 out of 1597 (39.5%) patients discontinued Sativex. The Kaplan-Meier estimates curve showed that 333 patients (20.8%) discontinued treatment at 4 weeks while 422 patients (26.4%) discontinued at 6 weeks. We found after adjusted modeling that a higher NRS score at T1 (adjHR 2.23, 95% 2.07-2.41, p<0.001) and a lower baseline NRS score (adjHR 0.51 95% CI 0.46-0.56, p<0.001) were predictive of treatment discontinuation. CONCLUSION: These data show that the first 6 weeks are useful in identifying those patients in which Sativex could be effective, thus avoiding the cost of longer term evaluation.

The corticospinal tract profile in amyotrophic lateral sclerosis.

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.

The relationship between regional microstructural abnormalities of the corpus callosum and physical and cognitive disability in relapsing-remitting multiple sclerosis.

Significant corpus callosum (CC) involvement has been found in relapsing-remitting multiple sclerosis (RRMS), even if conventional magnetic resonance imaging measures have shown poor correlation with clinical disability measures. In this work, we tested the potential of multimodal imaging of the entire CC to explain physical and cognitive disability in 47 patients with RRMS. Values of thickness, fractional anisotropy (FA) and mean diffusivity (MD) were extracted from 50 regions of interest (ROIs) sampled along the bundle. The relationships between clinical, neuropsychological and imaging variables were assessed by using Spearman's correlation. Multiple linear regression analysis was employed in order to identify the relative importance of imaging metrics in modeling different clinical variables. Regional fiber composition of the CC differentially explained the response variables (Expanded Disability Status Scale [EDSS], cognitive impairment). Increases in EDSS were explained by reductions in CC thickness and MD. Cognitive impairment was mainly explained by FA reductions in the genu and splenium. Regional CC imaging properties differentially explained disability within RRMS patients revealing strong, distinct patterns of correlation with clinical and cognitive status of patients affected by this specific clinical phenotype.