No abstract

Multiple Sclerosis patients' clinical symptoms do not correlate strongly with structural assessment done with traditional magnetic resonance images. However, its diagnosis and evaluation of the disease's progression are based on a combination of this imaging analysis complemented with clinical examination. Therefore, other biomarkers are necessary to better understand the disease. In this paper, we capitalize on machine learning techniques to classify relapsing-remitting multiple sclerosis patients and healthy volunteers based on machine learning techniques, and to identify relevant brain areas and connectivity measures for characterizing patients. To this end, we acquired magnetic resonance imaging data from relapsing-remitting multiple sclerosis patients and healthy subjects. Fractional anisotropy maps, structural and functional connectivity were extracted from the scans. Each of them were used as separate input features to construct support vector machine classifiers. A fourth input feature was created by combining structural and functional connectivity. Patients were divided in two groups according to their degree of disability and, together with the control group, three group pairs were formed for comparison. Twelve separate classifiers were built from the combination of these four input features and three group pairs. The classifiers were able to distinguish between patients and healthy subjects, reaching accuracy levels as high as 89% ± 2%. In contrast, the performance was noticeably lower when comparing the two groups of patients with different levels of disability, reaching levels below 63% ± 5%. The brain regions that contributed the most to the classification were the right occipital, left frontal orbital, medial frontal cortices and lingual gyrus. The developed classifiers based on MRI data were able to distinguish multiple sclerosis patients and healthy subjects reliably. Moreover, the resulting classification models identified brain regions, and functional and structural connections relevant for better understanding of the disease.

Multiple sclerosis (MS) is a chronic, inflammatory, and demyelinating disease of the central nervous system. It is a heterogeneous pathology that can follow different clinical courses, and the mechanisms that underlie the progression of the immune response across MS subtypes remain incompletely understood. Here, we aimed to determine differences in the immunological status among different MS clinical subtypes. Blood samples from untreated patients diagnosed with clinically isolated syndrome (CIS) (n = 21), different clinical forms of MS (n = 62) [relapsing–remitting (RRMS), secondary progressive, and primary progressive], and healthy controls (HCs) (n = 17) were tested for plasma levels of interferon (IFN)-γ, IL-10, TGF-β, IL-17A, and IL-17F by immunoanalysis. Th1 and Th17 lymphocyte frequencies were determined by flow cytometry. Our results showed that IFN-γ levels and the IFN-γ/IL-10 ratio were higher in CIS patients than in RRMS patients and HC. Th1 cell frequencies were higher in CIS and RRMS than in progressive MS, and RRMS had a higher Th17 frequency than CIS. The Th1/Th17 cell ratio was skewed toward Th1 in CIS compared to MS phenotypes and HC. Receiver operating characteristic statistical analysis determined that IFN-γ, the IFN-γ/IL-10 ratio, Th1 cell frequency, and the Th1/Th17 cell ratio discriminated among CIS and MS subtypes. A subanalysis among patients expressing high IL-17F levels showed that IL-17F and the IFN-γ/IL-17F ratio discriminated between disease subtypes. Overall, our data showed that CIS and MS phenotypes displayed distinct Th1- and Th17-related cytokines and cell profiles and that these immune parameters discriminated between clinical forms. Upon validation, these parameters might be useful as biomarkers to predict disease progression.

Background: Safety and effectiveness outcomes in Multiple Sclerosis (MS) patients receiving different disease-modifying therapies (DMT) and different types of vaccines against SARS-CoV-2 are limited. Growing evidence coming mainly from Israel, Europe and North America using mRNA and adenoviral vector vaccines has been published. Objectives: To assess the safety and humoral response of inactivated virus and mRNA vaccines against SARS-CoV-2 in patients with MS. Methods: Ongoing, multicentric, prospective, observational study performed between February and September 2021. Humoral response (antibodies against spike-1 protein) was determined at least 4 weeks after the complete schedule of anti-SARS-CoV-2 vaccines. Categorical outcome (positive/negative) and total antibody titres were recorded. Adverse events supposedly attributable to vaccination (AESAV) were collected. Results: 178 patients, 68% women, mean age 39.7±11.2 years, 123 received inactivated (Coronavac-Sinovac), 51 mRNA (Pfizer-BioNtech), and 4 adenoviral vector vaccines (CanSino n=2, Jonhson&Johnson-Jannsen n=1, Oxford-AstraZeneca n=1). Six patients had a history of COVID-19 before vaccination. Overall humoral response was observed in 66.9% (62.6% inactivated vs. 78.4% mRNA, p=0.04). Positive anti-S1-antibodies were observed in 100% of patients with no DMT (n=3), 100% with interferon/glatiramer-acetate (n=11), 100% with teriflunomide/dimethyl-fumarate (n=16), 100% with natalizumab (n=10), 100% with alemtuzumab (n=8), 90% with cladribine (n=10), and 88% with fingolimod (n=17), while 43% of patients receiving antiCD20 (n=99) were positive (38% inactivated vaccine vs. 59% mRNA vaccine, p=0.05). In the multivariate analysis including antiCD20 patients, the predictors for a positive humoral response were receiving the mRNA vaccine (OR 8.11 (1.79-36.8), p=0.007) and a lower number of total infusions (OR 0.44 (0.27-0.74) p=0.002. The most frequent AESAV was local pain (14%), with 4 (2.2%) patients experiencing mild-moderate relapses within 8 weeks of first vaccination compared to 11 relapses (6.2%) within the 8 weeks before vaccination (Chi-squared 3.41, p=0.06). Discussion: A higher humoral response rate was observed using the mRNA compared to the inactivated vaccine, while patients using antiCD20 had a significantly lower response rate, and patients using antiCD20 and fingolimod had lower antibody titres. In this MS patient cohort, inactivated and mRNA vaccines against SARS-CoV-2 appear to be safe, with no increase in relapse rate. This information may help guidelines including booster shots and types of vaccines in selected populations.