Evidence map›Paper›PMID 33162830›Full record

ArticleMediators of inflammation2020

Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis.

Ekaterina Martynova, Mehendi Goyal, Shikhar Johri, Vinay Kumar, Timur Khaibullin, Albert A Rizvanov, Subhash Verma, Svetlana F Khaiboullina, Manoj Baranwal

Open access · goldAbstract read
In one paragraph

Article in Mediators of inflammation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

  1. Pooled it
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  5. Review
  6. Central TYK2 inhibition identifies TYK2 as a key neuroimmune modulator.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  7. Article
  8. Artificial Intelligence and Multiple Sclerosis.Current neurology and neuroscience reports · 2024
    Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 3 countries.

Ekaterina MartynovaInstitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Tatarstan 420008, Russia.
Mehendi GoyalInstitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Tatarstan 420008, Russia.
Shikhar JohriAmity School of Engineering and Technology, Amity University, Jaipur, Rajasthan, India.
Vinay KumarDepartment of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, India.
Timur KhaibullinRepublican Clinical Neurological Canter, Republic of Tatarstan, Russia.
Albert A RizvanovInstitute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Tatarstan 420008, Russia.
Subhash VermaDepartment of Microbiology and Immunology, University of Nevada, Reno, NV 89557, USA.
Svetlana F KhaiboullinaDepartment of Microbiology and Immunology, University of Nevada, Reno, NV 89557, USA.ORCID https://orcid.org/0000-0002-5064-570X
Manoj BaranwalDepartment of Biotechnology, Thapar Institute of Engineering and Technology, Patiala 147004, India.ORCID https://orcid.org/0000-0002-6581-9346
Thapar Institute of Engineering & Technology · INKazan Federal University · RUUniversity of Nevada, Reno · USRepublican Oncological Clinical Dispensary · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMultiple sclerosis (MS) is a chronic debilitating disorder characterized by persisting damage to the brain caused by autoreactive leukocytes. Leukocyte activation is regulated by cytokines, which are readily detected in MS serum and cerebrospinal fluid (CSF).

objectiveSerum and CSF levels of forty-five cytokines were analyzed to identify MS diagnostic markers.

methodsCytokines were analyzed using multiplex immunoassay. ANOVA-based feature and Pearson correlation coefficient scores were calculated to select the features which were used as input by machine learning models, to predict and classify MS.

resultsTwenty-two and twenty cytokines were altered in CSF and serum, respectively. The MS diagnosis accuracy was ≥92% when any randomly selected five of these biomarkers were used. Interestingly, the highest accuracy (99%) of MS diagnosis was demonstrated when CCL27, IFN-

conclusionWe identified the set of cytokines from the serum and CSF that could be used for the MS diagnosis and classification.

Indexed as

AdultArea Under CurveBiomarkersCerebrospinal FluidChemokine CCL27CytokinesDecision TreesFemaleHumansImmunoassayInterferon-gammaInterleukin-4LeukocytesLymphocyte ActivationMachine LearningMaleBiomarkersCCL27 protein, humanChemokine CCL27CytokinesIFNG protein, humanIL4 protein, humanInterferon-gammaInterleukin-4

Identifiers

PMID33162830
PMCPMC7607285
OpenAlexW3096647341

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.