Antioxidant and Anti-inflammatory Diagnostic Biomarkers in Multiple Sclerosis: A Machine Learning Study

Elevated Oxidative Stress in Patients with Coexisting Multiple Sclerosis and Migraine: A Cross-Sectional Study

One potential association that is gaining increasing attention is the link between multiple sclerosis (MS) and migraine, which are suggested to frequently coexist in young patients. This is the first study to analyze the levels of multiple markers of oxidative stress in sociodemographically similar groups of patients with migraine, MS, and both MS and migraine. A single cross-sectional study was conducted at the Department of Neurology, Rzeszów University. The study included 110 participants, comprising 26 healthy controls, 24 subjects with migraines, 30 with MS, and 30 with both MS and migraine. Oxidative stress markers were measured in patients' serum. Patients with MS and migraines had statistically elevated levels of 3-nitrotyrosine, Amadori products, 4-hydroxy-nonenal, and oxidative damage to amino acids. Moreover, we observed reduced levels of thiol groups and total antioxidant capacity in the serum of patients with MS and migraines compared to healthy controls. The co-occurrence of migraines in MS leads to greater oxidative stress than MS alone. The impact of chronic oxidative stress on both MS and migraines may exacerbate symptoms and deteriorate the quality of life.

Autoimmune responses to myelin-associated proteins as diagnostic and prognostic biomarkers of relapsing-remitting multiple sclerosis: Associations with human herpesvirus-6 and Epstein-Barr virus reactivation

BACKGROUND

The pathogenesis of relapsing-remitting multiple sclerosis (RRMS) is linked to autoimmune attacks against myelin proteins, and reactivation of Epstein-Barr virus (EBV) and human herpesvirus 6 (HHV-6). However, the connection between viral reactivation and autoimmune biomarkers has remained unclear.

OBJECTIVES

To investigate immunoglobulin (Ig)G/IgA/IgM responses targeting myelin-related proteins in association with EBV and HHV-6 replication markers in RRMS.

METHODS

We recruited 55 patients with RRMS and 63 healthy controls and assessed IgG/IgA/IgM responses against seven myelin-related components, as well as EBV nuclear antigen 1 (EBNA-1) and deoxyuridine-triphosphate nucleotidohydrolase (dUTPases). Disability was evaluated using the Expanded Disability Status Scale (EDSS) and disease progression using the Multiple Sclerosis Severity Score (MSSS).

RESULTS

IgG/IgA/IgM levels targeting seven myelin-related proteins were significantly higher in RRMS than in controls. IgG against myelin basic protein (MBP) (IgG-MBP), IgM-myelin-associated glycoprotein (IgM-MAG)-37-60, IgA-MBP, and IgA-myelin-oligodendrocyte-glycoprotein (IgA-MOG-31-55) distinguished RRMS from controls with a predictive accuracy of 96.6 % (sensitivity = 95.7 %, specificity = 95.2 %) and an area under the ROC curve of 0.991. A large part of the variance in the EDSS (around 75 %) and MSSS score (62.8 %) was explained by IgG-MBP, IgM-MBP, IgA-MOG-31-55, and IgM-MAG. Part of the variance (47.4 %) in the IgG/IgA/IgM responses to myelin-related proteins was explained by immune responses to EBNA and dUTPases of EBV and HHV-6.

CONCLUSIONS

Autoimmune reactivities targeting myelin-related proteins are valuable biomarkers of RRMS and the severity and progression of RRMS. Reactivation of EBV and HHV-6 may trigger or maintain these autoimmune responses thereby impacting disease progression.

The effects of the MTHFR 677C>T (rs1801133) genetic variant on susceptibility and disability worsening in multiple sclerosis patients are mediated by homocysteine

BACKGROUND

Interactions between genetic and environmental variables contribute to the autoimmune inflammatory process in multiple sclerosis (MS). Elevated homocysteine levels, and vitamin D, vitamin B12, and folate deficiencies are some of the environmental factors associated with the pathogenesis of MS. Considering that the relationship between MTHFR 677C>T (rs1801133) genetic variant, homocysteine, and folate in patients with MS remains unclear and that their role were not extensively explored in the clinical course of the disease, we investigated whether this variant and plasma homocysteine and folate levels are associated with MS susceptibility, disability, disability progression, and inflammatory biomarkers.

METHODS

The case-control study included 163 patients with MS categorized using the Expanded Disability Status Scale (EDSS) as mild (EDSS<3) and moderate/high (EDSS≥3) disability, and 226 healthy controls (HC). Disability progression was evaluated using Multiple Sclerosis Severity Score (MSSS) and the MTHFR 677C>T variant was genotyped using real time polymerase chain reaction. The plasma levels of some inflammatory biomarkers were determined. Two new composed scores were proposed: the first, namely as inflammatory activity index (IAI), was entered as a latent vector extracted from the macrophage M1 + T helper (Th)1 + Th17 + Th2 + T regulatory (Treg) cytokines, + tumor necrosis factor (TNF)-α+ soluble TNF receptor (sTNFR)-1 + sTNFR2. The second score, namely MS-severity index was entered as a latent vector extracted from the EDSS + MSSS scores + MS diagnosis.

RESULTS

Patients with MS showed higher homocysteine and folate than controls (p < 0.001); homocysteine, and the M1, Th1, Th17, and Th2 Treg cytokine values were different between the three study groups and increased from HC to MS patients with mild disability and to MS patients with moderate/high disability (p < 0.0001). The levels of TNF-α and their soluble receptors sTNFR1 and sTNFR2 were higher in MS patients with EDSS≥3 than in the two other groups (EDSS<3 and HC) (p < 0.001). There was no association between the MTHFR 677 C > T genotypes and MS susceptibility, disability and disability progression (p > 0.05). Moreover, 21.8 % of the disability variance was explained by age, IAI and C-reactive protein (CRP) (all positively associated); 10.9 % of the disability progression variance was predicted by IAI and CRP (both positively) and 25-hydroxyvitamin D (negatively), whereas 54.4 % of the severity index (MS-EDSS-MSSS) was explained by the regression on age, IAI, homocysteine, folate, and CRP (all positively), and adiponectin, body mass index, and 25-hydroxyvitamin D (all negatively), female sex, and the MTHFR 677 TT genotype. In patients and controls, 16.6 % of the variance in the homocysteine was explained by the MTHFR 677 TT genotype and age (both positively), folate (negatively) and male sex.

CONCLUSION

The MTHFR 677C>T variant has an indirect effect on the increase in disability in patients with MS, which also depends on factors such as age, sex, ad folate status.

Big data and artificial intelligence applied to blood and CSF fluid biomarkers in multiple sclerosis

Artificial intelligence (AI) has meant a turning point in data analysis, allowing predictions of unseen outcomes with precedented levels of accuracy. In multiple sclerosis (MS), a chronic inflammatory-demyelinating condition of the central nervous system with a complex pathogenesis and potentially devastating consequences, AI-based models have shown promising preliminary results, especially when using neuroimaging data as model input or predictor variables. The application of AI-based methodologies to serum/blood and CSF biomarkers has been less explored, according to the literature, despite its great potential. In this review, we aimed to investigate and summarise the recent advances in AI methods applied to body fluid biomarkers in MS, highlighting the key features of the most representative studies, while illustrating their limitations and future directions.

Artificial Intelligence and Multiple Sclerosis

In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.

Changes of Target Essential Trace Elements in Multiple Sclerosis: A Systematic Review and Meta-Analysis

(1) Background: Multiple sclerosis (MS) is a chronic, complex, and demyelinating disease closely associated with altered levels of trace elements. Although the first studies into the role of trace elements in MS were published in the 1970s, for five decades it has remained unknown whether trace elements can be part of this heterogeneous neurological disease. (2) Materials and methods: To drive toward at a potential solution, we conducted a systematic review and meta-analysis to elucidate whether there were differences in circulating levels of neurologically important essential trace elements (Zn, Fe, Co, Cu, Mn, and Se) between MS cases and controls. (3) Results: This study revealed significantly lower serum/plasma Zn and Fe levels and higher Cu levels in MS-affected individuals compared to controls. At the same time, no significant differences were found between the MS cases and controls regarding their serum/plasma levels of Co, Mn, or Se. Thus, the loss of Fe and Zn should be considered in supplementation/nutrition strategies for MS patients. On the other hand, since high serum Cu levels indicate a burden on the bloodstreams of MS patients, Cu should be excluded from mineral supplement strategies. Furthermore, all three trace elements (Fe, Zn, and Cu) should be considered from an etiological point of view, and, most importantly, their levels in the bloodstreams of MS patients should be monitored. (4) Conclusions: This study highlights the way for personalized and targeted strategies in the management of MS.

Oxidative Stress Markers in Multiple Sclerosis

The pathogenesis of multiple sclerosis (MS) is not completely understood, but genetic factors, autoimmunity, inflammation, demyelination, and neurodegeneration seem to play a significant role. Data from analyses of central nervous system autopsy material from patients diagnosed with multiple sclerosis, as well as from studies in the main experimental model of multiple sclerosis, experimental autoimmune encephalomyelitis (EAE), suggest the possibility of a role of oxidative stress as well. In this narrative review, we summarize the main data from studies reported on oxidative stress markers in patients diagnosed with MS and in experimental models of MS (mainly EAE), and case-control association studies on the possible association of candidate genes related to oxidative stress with risk for MS. Most studies have shown an increase in markers of oxidative stress, a decrease in antioxidant substances, or both, with cerebrospinal fluid and serum/plasma malonyl-dialdehyde being the most reliable markers. This topic requires further prospective, multicenter studies with a long-term follow-up period involving a large number of patients with MS and controls.

Identification of crucial inflammaging related risk factors in multiple sclerosis

Background

Multiple sclerosis (MS) is an immune-mediated disease characterized by inflammatory demyelinating lesions in the central nervous system. Studies have shown that the inflammation is vital to both the onset and progression of MS, where aging plays a key role in it. However, the potential mechanisms on how aging-related inflammation (inflammaging) promotes MS have not been fully understood. Therefore, there is an urgent need to integrate the underlying mechanisms between inflammaging and MS, where meaningful prediction models are needed.

Methods

First, both aging and disease models were developed using machine learning methods, respectively. Then, an integrated inflammaging model was used to identify relative risk factors, by identifying essential "aging-inflammation-disease" triples. Finally, a series of bioinformatics analyses (including network analysis, enrichment analysis, sensitivity analysis, and pan-cancer analysis) were further used to explore the potential mechanisms between inflammaging and MS.

Results

A series of risk factors were identified, such as the protein homeostasis, cellular homeostasis, neurodevelopment and energy metabolism. The inflammaging indices were further validated in different cancer types. Therefore, various risk factors were integrated, and even both the theories of inflammaging and immunosenescence were further confirmed.

Conclusion

In conclusion, our study systematically investigated the potential relationships between inflammaging and MS through a series of computational approaches, and could present a novel thought for other aging-related diseases.

Stroke risk in multiple sclerosis: a critical appraisal of the literature

Observational studies suggest that the occurrence of stroke on multiple sclerosis (MS) patients is higher compared to the general population. MS is a heterogeneous disease that involves an interplay of genetic, environmental and immune factors. The occurrence of stroke is subject to a wide range of both modifiable and non-modifiable, short- and long-term risk factors. Both MS and stroke share common risk factors. The immune mechanisms that underlie stroke are similar to neurodegenerative diseases and are attributed to neuroinflammation. The inflammation in autoimmune diseases may, therefore, predispose to an increased risk for stroke or potentiate the effect of conventional stroke risk factors. There are, however, additional determinants that contribute to a higher risk and incidence of stroke in MS. Due to the challenges that are associated with their differential diagnosis, the objective is to present an overview of the factors that may contribute to increased susceptibility or occurrence of stroke in MSpatients by performing a review of the available to date literature. As both MS and stroke can individually detrimentally affect the quality of life of afflicted patients, the identification of factors that contribute to an increased risk for stroke in MS is crucial for the prompt implementation of preventative therapeutic measures to limit the additive burden that stroke imposes.

A systematic review of the application of machine-learning algorithms in multiple sclerosis

INTRODUCTION

The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

OBJECTIVE

We present a systematic review of the application of ML algorithms in MS.

MATERIALS AND METHODS

We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected.

CONCLUSIONS

After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

Evaluation of antioxidant parameters of multiple sclerosis patients' serum according to the disease course

INTRODUCTION

Multiple sclerosis (MS) is a chronic, demyelinating disease of the central nervous system. Its clinical courses are clinically isolated syndrome (CIS), relapsing remitting (RRMS), secondary progressive (SPMS), and primary progressive (PPMS). The differentiation of MS types is crucial for adequate treatment.

OBJECTIVES

To evaluate antioxidant parameters of MS patients' serum according to MS type.

MATERIALS AND METHODS

The study included 84 patients diagnosed with MS. The study group was divided into three subgroups corresponding to MS courses RRMS, SPMS, and PPMS. Sulfhydryl groups (SH), ceruloplasmin (CER), and superoxide dismutase (SOD) and its isoforms were identified in study participants' sera.

RESULTS

CuZnSOD levels were significantly higher in SPMS patients than in PPMS patients, but there was no difference between SMPS and treatment-naive PPMS patients. MnSOD activity was significantly lower in SPMS patients than in PPMS patients. Our results show that SH levels were decreased in SPMS patients compared with RRMS patients, but this difference was significant only for male participants. SH concentration was reversely correlated with age, BMI, disease duration, EDSS, and in smoking patients with pack-years. CER serum levels waere elevated in SPMS patients compared with RRMS patients, but this difference was significant only for male participants. Our results show correlation between CER and EDSS levels.

CONCLUSION

Oxidative stress plays a limited role in all disease stages, particularly in smokers as a confounding factor.

Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review

Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.

Chelation Therapy Associated with Antioxidant Supplementation Can Decrease Oxidative Stress and Inflammation in Multiple Sclerosis: Preliminary Results

An imbalance of oxy-inflammation status has been involved in axonal damage and demyelination in multiple sclerosis (MS). The aim of this study was to investigate the efficacy of an antioxidant treatment (calcium disodium ethylenediaminetetracetic acid-EDTA) chelation therapy associated with a micronutrient complex in MS patients. A total of 20 MS patients and 20 healthy subjects, enrolled as a control group (CTR), were recruited. We measured the plasma ROS production and total antioxidant capacity (TAC) by a direct assessment using Electron Paramagnetic Resonance; activities of the antioxidant system (thiols' redox status and enzymes); and the urinary presence of biomarkers of oxidative stress by immunoenzymatic assays. We also evaluated the levels of inflammation by plasmatic cytokines (TNFα, IL-1β, and IL-6) and assessed the sICAM levels, as well as the nitric oxide (NO) catabolism and transthyretin (TTR) concentration. Comparing CTR and MS, in the latter ROS production, oxidative damage, inflammatory biomarkers, and NO metabolite concentrations results were significantly higher, while TAC was significantly lower. Treatment in MS induced significant (p < 0.05) down-regulating of pro-inflammatory sICAM1, TNF-α, IL6, as well as biomarkers of lipid peroxidation and DNA damage production. The protective effect exhibited may occur by decreasing ROS production and increasing antioxidant capacity, turning into a more reduced thiols' status.

Zinc and Central Nervous System Disorders

Zinc (Zn²⁺) is the second most abundant necessary trace element in the human body, exerting a critical role in many physiological processes such as cellular proliferation, transcription, apoptosis, growth, immunity, and wound healing. It is an essential catalyst ion for many enzymes and transcription factors. The maintenance of Zn²⁺ homeostasis is essential for the central nervous system, in which Zn²⁺ is abundantly distributed and accumulates in presynaptic vesicles. Synaptic Zn²⁺ is necessary for neural transmission, playing a pivotal role in neurogenesis, cognition, memory, and learning. Emerging data suggest that disruption of Zn²⁺ homeostasis is associated with several central nervous system disorders including Alzheimer's disease, depression, Parkinson's disease, multiple sclerosis, schizophrenia, epilepsy, and traumatic brain injury. Here, we reviewed the correlation between Zn²⁺ and these central nervous system disorders. The potential mechanisms were also included. We hope that this review can provide new clues for the prevention and treatment of nervous system disorders.

Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis

BACKGROUND

The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging techniques. To overcome most of these difficulties, artificial intelligence (AI) can be used. A number of studies used AI models for multiple sclerosis (MS) diagnosis and reported diverse results. Therefore, we aim to perform a comprehensive systematic review and meta-analysis study on the role of AI in the diagnosis of MS.

METHODS

We performed a systematic search using four databases including PubMed, Scopus, Web of Science, and IEEE. Studies that applied deep learning or AI to the diagnosis of MS based on any modalities were considered eligible in our study. The accuracy, sensitivity, specificity, precision, and area under curve (AUC) were pooled with a random-effects model and 95% confidence interval (CI).

RESULTS

After the screening, 41 articles with 5989 individuals met the inclusion criteria and were included in our qualitative and quantitative synthesis. Our analysis showed that the overall accuracy among studies was 94% (95%CI: 93%, 96%). The pooled sensitivity and specificity were 92% (95%CI: 90%, 95%) and 93% (95%CI: 90%, 96%), respectively. Furthermore, our analysis showed 92% precision in MS diagnosis for AI studies (95%CI: 88%, 97%). Also, the overall pooled AUC was 93% (95%CI: 89%, 96%).

CONCLUSION

Overall, AI models can further improve our diagnostic practice in MS patients. Our results indicate that the use of AI can aid the clinicians in accurate diagnosis of MS and improve current diagnostic approaches as most of the parameters including accuracy, sensitivity, specificity, precision, and AUC were considerably high, especially when using MRI data.

Evaluation of Selected Oxidant/Antioxidant Parameters in Patients with Relapsing-Remitting Multiple Sclerosis Undergoing Disease-Modifying Therapies

The aim of this study was to evaluate oxidative stress parameters, specifically the concentration of advanced oxidation protein products (AOPP) and ferric-reducing antioxidant power (FRAP), in the serum of patients with relapsing-remitting multiple sclerosis (RRMS). We also analyzed the relationships between each parameter and selected clinical/laboratory multiple-sclerosis-related parameters. The study group comprised 204 patients with RRMS and 29 healthy, age-matched controls. The concentration of AOPP was significantly higher in the RRMS patients than in controls. ROC analysis showed the ability of AOPP to distinguish between the patients with RRMS and controls (the value of AUC was 94.8%, with a sensitivity of 89.69% and specificity of 89.3%). AOPP and FRAP were significantly higher in male than in female RRMS patients. Correlations were found between AOPP and the laboratory markers of inflammation. AOPP differed in the subgroups of patients treated with particular medications. Our findings indicate an increase in the markers of oxidative stress in the serum of RRMS patients, possibly linked with chronic inflammation. Gender and type of treatment affected the markers of oxidative stress.

Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities

Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients' data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.

An Enhanced Multiple Sclerosis Disease Diagnosis via an Ensemble Approach

Multiple Sclerosis (MS) is a disease attacking the central nervous system. According to MS Atlas's most recent statistics, there are more than 2.8 million people worldwide diagnosed with MS. Recently, studies started to explore machine learning techniques to predict MS using various data. The objective of this paper is to develop an ensemble approach for diagnosis of MS using gene expression profiles, while handling the class imbalance problem associated with the data. A hierarchical ensemble approach employing voting and boosting techniques is proposed. This approach adopts a heterogeneous voting approach using two base learners, random forest and support vector machine. Experiments show that our approach outperforms state-of-the-art methods, with the highest recorded accuracy being 92.81% and 93.5% with BoostFS and DEGs for feature selection, respectively. Conclusively, the proposed approach is able to efficiently diagnose MS using the gene expression profiles that are more relevant to the disease. The approach is not merely an ensemble classifier outperforming previous work; it also identifies differentially expressed genes between normal samples and patients with multiple sclerosis using a genome-wide expression microarray. The results obtained show that the proposed approach is an efficient diagnostic tool for MS.

Blood Trace Element Status in Multiple Sclerosis: a Systematic Review and Meta-analysis

The aim of this meta-analysis was to investigate whether the blood concentrations of patients with multiple sclerosis (MS) are associated with those of the healthy control group in terms of trace elements including zinc (Zn), iron (Fe), manganese (Mn), magnesium (Mg), selenium (Se), and copper (Cu). A comprehensive search was performed in online databases including PubMed, Scopus, Embase, and Web of Science for studies, which have addressed trace elements in MS up to July 23, 2020. The chi-square test and I² statistic were utilized to evaluate inter-study heterogeneity across the included studies. Weighted mean differences (WMDs) and corresponding 95% CI were considered as a pooled effect size (ES). Twenty-seven articles (or 32 studies) with a total sample comprised of 2895 participants (MS patients (n = 1567) and controls (n = 1328)) were included. Pooled results using random-effects model indicated that the levels of Zn (WMD = - 7.83 mcg/dl, 95% CI = - 12.78 to - 2.87, Z = 3.09, P = 0.002), and Fe (WMD = - 13.66 mcg/dl, 95% CI = - 23.13 to - 4.19, Z = 2.83, P = 0.005) were significantly lower in MS patients than in controls. However, it was found that levels of Mn (WMD = 0.03 mcg/dl, 95% CI = 0.01 to 0.04, Z = 2.89, P = 0.004) were significantly higher in MS patients. Yet, no significant differences were observed in the levels of Mg, Se, and Cu between both groups. This meta-analysis revealed that the circulating levels of Zn and Fe were significantly lower in MS patients and that Mn level was significantly higher than those in the control group. However, it was found that there was no significant difference between MS patients and controls with regard to levels of Mg, Se, and Cu.

Advanced Oxidative Protein Products Role in Multiple Sclerosis: a Systematic Review and Meta-analysis

Multiple sclerosis (MS) is an autoimmune-mediated disease that damages the central nervous system. MS pathophysiological features are not entirely understood, but the increase of reactive oxygen species (ROS) possibly causes myelin and oligodendrocyte degeneration. ROS-increased production generates new compounds through oxidative modifications, including advanced oxidative protein products (AOPPs). The AOPPs are oxidative stress biomarkers and inflammatory mediators commonly formed by hypochlorous acid oxidative action on albumin. Considering that AOPPs accumulation produces ROS and induces neuronal apoptosis, these may represent a new target for drug development to MS treatment and a possible biomarker to monitor the severity of the disease. Thus, this review aims to investigate if there is an alteration in the AOPPs levels in MS and its possible involvement in patient disability. The second objective is to analyze whether drugs or compounds used in MS treatment could modify the AOPPs levels. The protocol was registered in PROSPERO (CRD42020203268). The databases' search yielded 327 articles. We excluded 259 duplicated articles and evaluated 68 articles by the title and abstract. We full-text analyzed 17 articles and included 13 articles. The AOPPs levels were increased in not-treated MS patients. Furthermore, the increase in disability status was associated with AOPPs accumulation in not-treated MS patients. Additionally, the AOPPs levels were reduced in MS patients after treatment. Therefore, AOPPs seem to play a role in MS pathophysiology and may become a new target for drug development and help MS diagnosis or treatment follow-up.

Nutritive Manganese and Zinc Overdosing in Aging C. elegans Result in a Metallothionein-Mediated Alteration in Metal Homeostasis

SCOPE

Manganese (Mn) and zinc (Zn) are not only essential trace elements, but also potential exogenous risk factors for various diseases. Since the disturbed homeostasis of single metals can result in detrimental health effects, concerns have emerged regarding the consequences of excessive exposures to multiple metals, either via nutritional supplementation or parenteral nutrition. This study focuses on Mn-Zn-interactions in the nematode Caenorhabditis elegans (C. elegans) model, taking into account aspects related to aging and age-dependent neurodegeneration.

METHODS AND RESULTS

Chronic co-exposure of C. elegans to Mn and Zn increases metal uptake, exceeding levels of single metal exposures. Supplementation with Mn and/or Zn also leads to an age-dependent increase in metal content, a decline in overall mRNA expression, and metal co-supplementation induced expression of target genes involved in Mn and Zn homeostasis, in particular metallothionein 1 (mtl-1). Studies in transgenic worms reveal that mtl-1 played a prominent role in mediating age- and diet-dependent alterations in metal homeostasis. Metal dyshomeostasis is further induced in parkin-deficient nematodes (Parkinson's disease (PD) model), but this did not accelerate the age-dependent dopaminergic neurodegeneration.

CONCLUSIONS

A nutritive overdose of Mn and Zn can alter interactions between essential metals in an aging organism, and metallothionein 1 acts as a potential protective modulator in regulating homeostasis.

Exercise rapidly alters proteomes in mice following spinal cord demyelination

Exercise affords broad benefits for people with multiple sclerosis (PwMS) including less fatigue, depression, and improved cognition. In animal models of multiple sclerosis (MS), exercise has been shown to improve remyelination, decrease blood-brain barrier permeability and reduce leukocyte infiltration. Despite these benefits many PwMS refrain from engaging in physical activity. This barrier to participation in exercise may be overcome by uncovering and describing the mechanisms by which exercise promotes beneficial changes in the central nervous system (CNS). Here, we show that acute bouts of exercise in mice profoundly alters the proteome in demyelinating lesions. Following lysolecithin induced demyelination of the ventral spinal cord, mice were given immediate access to a running wheel for 4 days. Lesioned spinal cords and peripheral blood serum were then subjected to tandem mass tag labeling shotgun proteomics workflow to identify alteration in protein levels. We identified 86 significantly upregulated and 85 downregulated proteins in the lesioned spinal cord as well as 14 significantly upregulated and 11 downregulated proteins in the serum following acute exercise. Altered pathways following exercise in demyelinated mice include oxidative stress response, metabolism and transmission across chemical synapses. Similar acute bout of exercise in naïve mice also changed several proteins in the serum and spinal cord, including those for metabolism and anti-oxidant responses. Improving our understanding of the mechanisms and duration of activity required to influence the injured CNS should motivate PwMS and other conditions to embrace exercise as part of their therapy to manage CNS disability.

A systematic review of the application of machine-learning algorithms in multiple sclerosis

INTRODUCTION

The applications of artificial intelligence, and in particular automatic learning or "machine learning" (ML), constitute both a challenge and a great opportunity in numerous scientific, technical, and clinical disciplines. Specific applications in the study of multiple sclerosis (MS) have been no exception, and constitute an area of increasing interest in recent years.

OBJECTIVE

We present a systematic review of the application of ML algorithms in MS.

MATERIALS AND METHODS

We used the PubMed search engine, which allows free access to the MEDLINE medical database, to identify studies including the keywords "machine learning" and "multiple sclerosis." We excluded review articles, studies written in languages other than English or Spanish, and studies that were mainly technical and did not specifically apply to MS. The final selection included 76 articles, and 38 were rejected.

CONCLUSIONS

After the review process, we established 4 main applications of ML in MS: 1) classifying MS subtypes; 2) distinguishing patients with MS from healthy controls and individuals with other diseases; 3) predicting progression and response to therapeutic interventions; and 4) other applications. Results found to date have shown that ML algorithms may offer great support for health professionals both in clinical settings and in research into MS.

Superoxide dismutase (SOD), advanced oxidation protein products (AOPP), and disease-modifying treatment are related to better relapse recovery after corticosteroid treatment in multiple sclerosis

OBJECTIVES

The aim of our study was to analyze oxidative stress (OS) markers in multiple sclerosis (MS) patients during relapse and remission and to evaluate the effects of corticosteroid relapse treatment on oxidative status, and also to determine possible relationship between OS markers and relapse disability recovery after corticosteroid treatment.

METHODS

Our study included 118 MS patients, (59 relapse/59 remission) 70 females and 48 males, mean age 40.2 ± 9.4 years, and 88 matched healthy controls. Undergoing disease-modifying therapy (DMT) was present in 30.5% of relapse and 88% of remission MS patients. We analyzed in plasma/serum the following: pro-oxidative-antioxidative balance (PAB), nitrates and nitrites (NO₃ + NO₂), malondialdehyde (MDA), advanced oxidation protein products (AOPP) superoxide dismutase (SOD), catalase (CAT), uric acid, bilirubin, albumin, and transferrin in all patients and additionally after corticosteroid relapse treatment. Neurological disability was measured using the Extended Disability Status Scale (EDSS).

RESULTS

Better clinical recovery after relapse treatment was associated with increased baseline SOD, decreased AOPP, and ongoing DMT (all p < 0.05). There was no difference between OS markers in relapse and remission. MS patients had higher MDA, NO₃ + NO₂, PAB, SOD, CAT, lower AOPP, uric acid, albumin, bilirubin, and transferrin compared to controls (all p < 0.05). Corticosteroids caused significant decrease of all OS markers (all p < 0.05).

CONCLUSION

Increased baseline antioxidative activity of SOD and decreased baseline levels of pro-oxidant AOPP along with ongoing DMT were related to better clinical recovery after corticosteroid relapse treatment. Increase of pro-oxidants and antioxidant enzyme activity in relapse and remission confirms ongoing oxidative injury irrelevant of MS clinical presentation.

The MAP kinase inhibitor PD98059 reduces chromosomal instability in the autoimmune encephalomyelitis SJL/J-mouse model of multiple sclerosis

Multiple sclerosis (MS), a disease in which the immune system attacks nerve cells, has been associated with both genetic and environmental risk factors. We observed increased micronucleus (MN) formation in SJL/J mouse experimental autoimmune encephalomyelitis (EAE), an animal model of MS. Most of these MN were due to chromosomal loss. Increased activation of MAP kinases, which leads to disruption of the mitotic spindle and improper segregation of chromosomes, is associated with MS. MAP kinase inhibitors, such as PD98059, may therefore be beneficial for MS. In the EAE model, PD98059 treatment reduced adverse effects, including MN formation, lipid peroxidation, and GSH oxidation. Interventions that mitigate chromosomal instability may have therapeutic value in MS.