Evidence map›Paper›PMID 25601865›Full record

ReviewFrontiers in immunology2014

Immunological Markers for PML Prediction in MS Patients Treated with Natalizumab.

Caroline Antoniol, Bruno Stankoff

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
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

16 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Biomarkers in Multiple Sclerosis.Cold Spring Harbor perspectives in medicine · 2019
    Review
  5. Article
  6. Review
  7. Observational
  8. Article
  9. Article
  10. Multiple sclerosis: genetics, biomarkers, treatments.Current opinion in neurology · 2016
    Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
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

2 authors.

Caroline AntoniolAP-HP, Hôpital Saint-Antoine , Paris , France ; Centre Hospitalier Universitaire de Dijon, Université de Bourgogne , Dijon , France.
Bruno StankoffAP-HP, Hôpital Saint-Antoine , Paris , France ; Sorbonne Universités, Université Pierre et Marie Curie, UMR S 1127, CNRS UMR 7225, and l'Institut du Cerveau et de la Moelle Épinière (ICM) , Paris , France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Natalizumab (NTZ), a monoclonal antibody recognizing the alpha4 integrin chain, has been approved for the treatment of active multiple sclerosis, but expose to the onset of a rare side effect, progressive multifocal leukoencephalopathy (PML). Estimating the individual risk of PML in NTZ-treated patients is a major challenge, and therapeutic strategies are mainly guided by the overall PML risk assessed by identified risk factors: JC virus (JCV) seropositivity, treatment duration (with peak incidence after 24 months), and the previous use of immunosuppressive therapies. Given that this stratification does not yet allow a precise individual prediction of PML, other predictive markers are needed, and several immunological biomarkers have been described. Quantification of anti-JCV antibody levels may improve individual predictive value, with higher baseline titers indicating increased risk. Other immunological biomarkers such as leukocyte cell membrane markers (CD49d, CD11a, and CD62L), detection of circulating JCV-specific activated T effector memory cells (TEM) or genetic screening have been proposed. In this review, we discuss how recent progress in immunology has paved the way for «new combined monitoring», which will include immunological screening, in NTZ-treated patients.

Indexed as

effector memory T-cellsJC virusmultiple sclerosisPMLrisk stratificationselectin

Identifiers

PMID25601865
PMCPMC4283628

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Registered trials

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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.