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Background: The McGill Quality of Life Questionnaire-Revised (MQoL-R) is the gold standard for assessing QoL in end-of-life, chronic patients; however, an Italian standardization is lacking., Objective: This study aimed at assessing the psychometric properties of the Italian MQoL-R in patients with chronic neurological/oncological conditions., Methods: 177 inpatients with life-threatening, chronic neurological/oncological conditions were consecutively recruited in 8 clinics in Northern/Southern Italy were administered the MQoL-R and the Karnofsky Performance Status (KPS). Factorial structure (Confirmatory Factor Analysis, CFA), reliability (Cronbach's α) and construct validity against the KPS (Pearson's coefficients) were examined., Results: The four-factor model (Physical, Psychological, Existential and Social subscales) was met (comparative fit index = .93; root mean square error of approximation = .07), with all items significantly loading on respective subscales. Internal consistency was good for both the whole scale (Cronbach's α = .83) and subscales ( range  = .6-.85). The KPS was unrelated to MQoL-R measures, except for the Physical subscale ( r  = .24)., Conclusions: The Italian MQoL-R is a valid and reliable tool to assess QoL in end-of-life, both neoplastic and neurological, chronic inpatients undergoing palliative care, whose adoption is thus encouraged in both clinical practice and research addressed to such populations.

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 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.

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., (Copyright © 2017 Elsevier B.V. All rights reserved.)

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.