ArticleMolecular medicine reports2019
Identification of CD4+ T cell biomarkers for predicting the response of patients with relapsing‑remitting multiple sclerosis to natalizumab treatment.
Article in Molecular medicine reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- 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
- Pooled it
- Pooled it
- Artificial Intelligence Algorithm Based on Genetics to Predict Responses to Interferon-Beta Treatment in Multiple Sclerosis Patients.Bioengineering (Basel, Switzerland) · 2026Article
- Big data and artificial intelligence applied to blood and CSF fluid biomarkers in multiple sclerosis.Frontiers in immunology · 2024Review
- A Deep Learning Approach for Predicting Multiple Sclerosis.Micromachines · 2023Article
- Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles.Scientific reports · 2022Article
- Review
- RNA Sequencing of CD4Journal of molecular neuroscience : MN · 2021Article
- Transcriptomic Data Analysis Reveals a Down-Expression ofBrain sciences · 2021Article
- Genetic variations in theExperimental and therapeutic medicine · 2021Article
- GNG13 Is a Potential Marker of the State of Health of Alzheimer's Disease Patients' Cerebellum.Journal of molecular neuroscience : MN · 2021Article
- Profiling of inhibitory immune checkpoints in glioblastoma: Potential pathogenetic players.Oncology letters · 2020Article
- Upregulation of Tolerogenic Pathways by the Hydrogen Sulfide Donor GYY4137 and Impaired Expression of HAntioxidants (Basel, Switzerland) · 2020Article
- Transcriptomic Analysis Reveals Abnormal Expression of Prion Disease Gene Pathway in Brains from Patients with Autism Spectrum Disorders.Brain sciences · 2020Article
- Analysis of circular RNA-associated competing endogenous RNA network in breast cancer.Oncology letters · 2020Article
- Impaired Expression of Tetraspanin 32 (TSPAN32) in Memory T Cells of Patients with Multiple Sclerosis.Brain sciences · 2020Article
- Neuroprotective Mechanisms of Three Natural Antioxidants on a Rat Model of Parkinson's Disease: A Comparative Study.Antioxidants (Basel, Switzerland) · 2020Article
- In Silico and In Vivo Analysis of IL37 in Multiple Sclerosis Reveals Its Probable Homeostatic Role on the Clinical Activity, Disability, and Treatment with Fingolimod.Molecules (Basel, Switzerland) · 2019Article
- Effects of Treatment with the Hypomethylating Agent 5-aza-2'-deoxycytidine in Murine Type II Collagen-Induced Arthritis.Pharmaceuticals (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system of autoimmune etiopathogenesis, and is characterized by various neurological symptoms. Glatiramer acetate and interferon‑β are administered as first‑line treatments for this disease. In non‑responsive patients, several second‑line therapies are available, including natalizumab; however, a percentage of MS patients does not respond, or respond partially. Therefore, it is of the utmost importance to develop a diagnostic test for the prediction of drug response in patients suffering from complex diseases, such as MS, where several therapeutic options are already available. By a machine learning approach, the UnCorrelated Shrunken Centroid algorithm was applied to identify a subset of genes of CD4+ T cells that may predict the pharmacological response of relapsing‑remitting MS patients to natalizumab, before the initiation of therapy. The results from the present study may provide a basis for the design of personalized therapeutic strategies for patients with MS.
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Registered trials
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