ArticleFrontiers in neurology2019
Computational Intelligence Technique for Prediction of Multiple Sclerosis Based on Serum Cytokines.
Article in Frontiers in neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.BMC medical informatics and decision making · 2022Pooled it
- Artificial Intelligence Algorithm Based on Genetics to Predict Responses to Interferon-Beta Treatment in Multiple Sclerosis Patients.Bioengineering (Basel, Switzerland) · 2026Article
- Rethinking MS Therapeutics: From Disease Pathogenesis Mechanisms to AI-Driven Drug Discovery.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2026Review
- Predicting multiple sclerosis prognosis using AI and machine learning: integrating clinical, immunological, and radiological variables.Frontiers in neurology · 2025Article
- Big data and artificial intelligence applied to blood and CSF fluid biomarkers in multiple sclerosis.Frontiers in immunology · 2024Review
- Cerebrospinal fluid cytokines and chemokines exhibit distinct profiles in bacterial meningitis and viral meningitis.Experimental and therapeutic medicine · 2023Article
- A Deep Learning Approach for Predicting Multiple Sclerosis.Micromachines · 2023Article
- Prophylactic effects of cucurbitacin B in the EAE Model of multiple sclerosis by adjustment of STAT3/IL-23/IL-17 axis and improvement of neuropsychological symptoms.Metabolic brain disease · 2022Article
- Multiplex Analysis of Serum Cytokine Profiles in Systemic Lupus Erythematosus and Multiple Sclerosis.International journal of molecular sciences · 2022Article
- Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles.Scientific reports · 2022Article
- Analysis of herpesvirus infection and genome single nucleotide polymorphism risk factors in multiple sclerosis, Volga federal district, Russia.Frontiers in immunology · 2022Article
- Autoreactive lymphocytes in multiple sclerosis: Pathogenesis and treatment target.Frontiers in immunology · 2022Review
- Role of artificial intelligence in MS clinical practice.NeuroImage. Clinical · 2022Review
- Role of the gut microbiome in multiple sclerosis: From etiology to therapeutics.International review of neurobiology · 2022Review
- An evaluation of the recognised systemic inflammatory biomarkers of chronic sub-optimal inflammation provides evidence for inflammageing (IFA) during multiple sclerosis (MS).Immunity & ageing : I & A · 2021Review
- Digital Twins for Multiple Sclerosis.Frontiers in immunology · 2021Review
- The emergence of new trends in clinical laboratory diagnosis.Saudi medical journal · 2020Review
- Analysis of Polymorphisms rs7093069-IL-2RA, rs7138803-FAIM2, and rs1748033-PADI4 in the Group of Adolescents With Autoimmune Thyroid Diseases.Frontiers in endocrinology · 2020Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple sclerosis (MS) is a neurodegenerative disease characterized by lesions in the central nervous system (CNS). Inflammation and demyelination are the leading causes of neuronal death and brain lesions formation. The immune reactivity is believed to be essential in the neuronal damage in MS. Cytokines play important role in differentiation of Th cells and recruitment of auto-reactive B and T lymphocytes that leads to neuron demyelination and death. Several cytokines have been found to be linked with MS pathogenesis. In the present study, serum level of eight cytokines (IL-1β, IL-2, IL-4, IL-8, IL-10, IL-13, IFN-γ, and TNF-α) was analyzed in USA and Russian MS to identify predictors for the disease. Further, the model was extended to classify MS into remitting and non-remitting by including age, gender, disease duration, Expanded Disability Status Scale (EDSS) and Multiple Sclerosis Severity Score (MSSS) into the cytokines datasets in Russian cohorts. The individual serum cytokines data for the USA cohort was generated by Z score percentile method using R studio, while serum cytokines of the Russian cohort were analyzed using multiplex immunoassay. Datasets were divided into training (70%) and testing (30%). These datasets were used as an input into four machine learning models (support vector machine, decision tree, random forest, and neural networks) available in R programming language. Random forest model was identified as the best model for diagnosis of MS as it performed remarkable on all the considered criteria i.e., Gini, accuracy, specificity, AUC, and sensitivity. RF model also performed best in predicting remitting and non-remitting MS. The present study suggests that the concentration of serum cytokines could be used as prognostic markers for the prediction of MS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.