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The diagnosis and quantification of Multiple Sclerosis (MS) have typically depended on skilled doctors recognizing visual patterns, such as Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG), which resulted in a costly, time-consuming and non-reproducible process. The application of Machine Learning (ML) in MS diagnosis has been getting a lot of attention in the last few years due to the volume of scientific data, the heterogeneity of disease courses, and the variety of diagnostic methods. The EEG has the capability to detect important changes in the brain’s inherent electrical activity, which are influenced by changes in the neural network connections associated with inflammatory demyelinating and neural damage characteristic of the MS. Utilizing multimodal machine learning over the clinically available data may be a contemporary strategy with amazing potential to facilitate early diagnosis. Considering recent EEG investigations as well as the accessibility to their datasets would be the initial steps in utilizing the ML in MS diagnosis. This paper provides a systematic review of the latest techniques for MS diagnostic based on PRISMA guidelines with the prospect of ML application in their investigations. The goal is to find if EEG could be considered a robust and accurate technique for MS diagnosis. In accordance with PRISMA guidelines, we consider 111 papers. In our review, 404 people are considered including 209 with MS and 195 healthy controls. As a result, we generated an updated investigation looking at the ML approaches as well as utilizing EEG as an accurate, but less often used method to help with early diagnosis of MS. We summarize, analyze, discuss, and synthesize the recently published works, current trends, and open research issues. The review also points out knowledge gaps about the necessity of validating results and addressing constraints such as limited sample number. Our investigation proves that the precision of the supervised strategies such as $k$NN and SVM is typically higher than that of the unsupervised strategies. On the other hand, by utilizing various techniques such as splitting EEG signal sub-bands, signal windowing, and identifying effective features from the data analysis approaches, we have been able to achieve significant classification accuracy higher than 99%. According to the high degree of accuracy of the results, this approach is becoming the focal point of the research works. The high diagnostic accuracy of the proposed machine learning techniques on the EEG signal analysis shows its potential capacity to become a more widespread procedure as the MS diagnostic techniques. To develop the reliability of these methods, gathering, and analyzing more EEG signals from MS patients and creating more suitable EEG protocols are essential in future studies. In the same way, for the dynamic and online analyses of the EEG signals, in-depth studies are needed to determine more effective EEG protocols and machine-learning techniques.

Despite several automated strategies for identification/segmentation of Multiple Sclerosis (MS) lesions in Magnetic Resonance Imaging (MRI) being developed, they consistently fall short when compared to the performance of human experts. This emphasizes the unique skills and expertise of human professionals in dealing with the uncertainty resulting from the vagueness and variability of MS, the lack of specificity of MRI concerning MS, and the inherent instabilities of MRI. Physicians manage this uncertainty in part by relying on their radiological, clinical, and anatomical experience. We have developed an automated framework for identifying and segmenting MS lesions in MRI scans by introducing a novel approach to replicating human diagnosis, a significant advancement in the field. This framework has the potential to revolutionize the way MS lesions are identified and segmented, being based on three main concepts: (1) Modeling the uncertainty; (2) Use of separately trained Convolutional Neural Networks (CNNs) optimized for detecting lesions, also considering their context in the brain, and to ensure spatial continuity; (3) Implementing an ensemble classifier to combine information from these CNNs. The proposed framework has been trained, validated, and tested on a single MRI modality, the FLuid-Attenuated Inversion Recovery (FLAIR) of the MSSEG benchmark public data set containing annotated data from seven expert radiologists and one ground truth. The comparison with the ground truth and each of the seven human raters demonstrates that it operates similarly to human raters. At the same time, the proposed model demonstrates more stability, effectiveness and robustness to biases than any other state-of-the-art model though using just the FLAIR modality.

Matrine (MAT), a quinolizidine alkaloid derived from the herb Radix Sophorae Flave, has been suggested to possess immunomodulatory characteristics; however, whether it is effective in multiple sclerosis (MS), an autoimmune disease of the central nervous system (CNS), is not known. Our aim was to bridge this gap by investigating the possible therapeutic effects of MAT on experimental autoimmune encephalomyelitis (EAE), an animal model of MS. We have found that, compared to the untreated controls, MAT-treated rats showed a significant decrease in clinical scores, in CNS infiltration of inflammatory cells (including CD4⁺, CD8⁺ T cells and macrophages) and demyelination. Furthermore, serum levels of IL-23 and IL-17 showed a marked reduction after MAT treatment, particularly in rats treated with higher doses of MAT. This study demonstrates that administration of MAT, as a natural compound, might be a novel therapy for autoimmune disorders such as MS.

The regulatory effect of Liuwei Dihuang Pills (LDP) was studied on cytokines in mice with experimental autoimmune encephalomyelitis (EAE), a model for human multiple sclerosis (MS), induced by immunization with MOG_35-55 and complete Freund's adjuvant (CFA) supplemented with pertussis toxin (PTX). LDP was administrated orally for 40 days, and prednisone acetate (PA) was used as a control. The pathological changes in the spinal cords of mice were observed by light microscope with hematoxylin-eosin (HE) staining and transmission electron microscope (TEM). The protein and mRNA expression of tumor necrosis factor-alpha (TNF-α) and transforming growth factor-beta (TGF-β) in the spinal cords were assessed by immunohistochemistry and RT-PCR assay, and the cyclic adenosine monophosphate (cAMP) in mice plasma was measured by radioimmunoassay (RIA) on days 12, 25 and 40 post-immunization (PI). The results showed that inflammatory cells, demyelination and axonal loss were reduced, and that the protein and mRNA expression of TNF-α and the ratio of TNF-α/TGF-β were obviously decreased, to different extents. However, the levels of cAMP were enhanced in LDP-treated groups. These findings suggested that LDP regulates the cytokine balance in favor of T helper 1 (Th1)/regulatory T (Treg) cells, which depend on enhancement of cAMP levels. LDP has a potential role in the treatment of MS and other demyelinating diseases of the central nervous system.

In this paper, we present a mathematical study for the development of Multiple Sclerosis in which a spatio-temporal kinetic theory model describes, at the mesoscopic level, the dynamics of a high number of interacting agents. We consider both interactions among different populations of human cells and the motion of immune cells, stimulated by cytokines. Moreover, we reproduce the consumption of myelin sheath due to anomalously activated lymphocytes and its restoration by oligodendrocytes. Successively, we fix a small time parameter and assume that the considered processes occur at different scales. This allows us to perform a formal limit, obtaining macroscopic reaction–diffusion equations for the number densities with a chemotaxis term. A natural step is then to study the system, inquiring about the formation of spatial patterns through a Turing instability analysis of the problem and basing the discussion on the microscopic parameters of the model. In particular, we get spatial patterns oscillating in time that may reproduce brain lesions characteristic of different phases of the pathology.

Multiple sclerosis (MS) is a type of degenerative syndrome that causes impaired physical function, decreased walking, imbalance, spasticity, sensory impairment, muscle weakness, fatigue, and demyelination of the central nervous system. The purpose of this review was to critically examine available studies on the efficacy of whole-body vibration (WBV) in patients with MS during rehabilitation training to increase strength, balance, and functional mobility. An organized literature search was performed on databases from various sources, including PubMed, MEDLINE, CINAHL, and EMBASE, to identify relevant randomized clinical trials (RCTs). Eight studies were finally selected based on exclusion and inclusion criteria. Attempts were made to identify factors affecting the improvement in muscle strength, balance, and functional mobility in MS patients as a result of WBV. A meta-analysis was performed if two or more studies measured the same outcome of interest. The meta-analysis found that the WBV intervention showed significant improvement over control groups in Body Balance Score (BBS) (MD = $-$2.86, 95%CI = $-$5.29 $-$ 0.43; $Z=2.31$, $p=0.02$, heterogeneity ($I^2=91$%). In addition, walking endurance (6MWT) favored control groups over WBV intervention (MD $=59.86$, 95%CI $=20.3$$-$ 99.41; $Z$ = 2.97, $p=0.003$). Timed-Up-and-Go Test (TUG) and Expanded Disability Status Scale (EDSS) ($p>0.05$) had no significant effect on WBV. Restoration of balance and functional mobility appeared to respond better to WBV with additional exercise protocols compared to WBV alone. Although there is evidence of an overall effect of WBV on strength and some measures of balance and mobility, its impact remains inconclusive. Therefore, more robust RCTs examining exposure to WBV on balance and functional mobility in patients with MS are warranted.

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Impaired balance is one of the most disabling multiple sclerosis (MS) symptoms. It is known that, in the presence of predictable perturbations, the central nervous system (CNS) utilizes both anticipatory (APAs) and compensatory (CPAs) postural adjustments to maintain balance. The main purpose of this study was to investigate the relationship between APAs and CPAs during self-induced postural perturbation in patients with MS. Participants performed a load release task while standing on a force platform. Electrical activity of six leg and trunk muscles, as well as displacements of the center of pressure (COP), was recorded. The results revealed significant APAs deficits in MS patients reflected in short APAs duration and reduced magnitude. The reduced APAs were not accompanied by significant compensatory muscle activity. It can be concluded that there is an impairment of feed-forward postural control in MS, and feedback-based mechanisms (CPAs) are unable to compensate for these APA deficits. These results should be considered in the rehabilitation programs for balance training of MS patients.

The aim of this study is to investigate if there is any nonlinearity in the magnetoencephalographic recordings of patients with multiple sclerosis in comparison with controls in order to find out the differences in the mechanisms underlying their brain waves. Five multiple sclerosis patients and five controls were included in this study. Chaotic activity of multiple sclerosis patients is lower than in the normal brain. Nonlinear analysis may offer fertile perspectives for understanding the features of patients with multiple sclerosis.