Evidence map›Paper›PMID 39871711›Full record

ArticleMultiple sclerosis (Houndmills, Basingstoke, England)2025

The Barancik award lecture: Multi-disciplinary research will be the key to stop, restore, and end MS.

Sergio E Baranzini

Abstract read
In one paragraph

Article in Multiple sclerosis (Houndmills, Basingstoke, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–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

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. 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

1 author.

Sergio E BaranziniDepartment of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0003-0067-194X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The past 25 years have brought extraordinary advances in our understanding of MS pathogenesis and the subsequent development of effective therapies. Collaborative genetics efforts have uncovered the association of 236 common DNA variants with disease susceptibility and the first association with disease severity, paving the way to more effective therapies, particularly for progressive forms of the disease. In parallel, and in addition to established environmental disease triggers or modifiers, new collaborative work has revealed new associations with components of the gut microbiome. This research opened a new and exciting prospect for exploring the gut-brain axis, with the potential to also provide new pharmacologic targets and diet-based therapies. Finally, with the availability of massive amounts of information and unprecedented computer power, a new wave of artificial intelligence (AI)-based research is sprawling. These investigations will result in statistically powerful predictive models to identify individuals at risk even years before the disease is clinically apparent. Furthermore, using approaches like semantic representation and causal inference, some of these approaches will be explainable in biomedical terms, thus making them trusted and facilitating their implementation in the clinical setting. The common thread that characterizes all of these advances is multi-disciplinary collaboration among scientists in the form of formal consortia, working groups, or ad hoc partnerships. This may be the "secret sauce" of modern science and the best strategy to stop, restore, and end MS.

Indexed as

Interdisciplinary ResearchMultiple SclerosisArtificial IntelligenceAwards and PrizesGastrointestinal MicrobiomeHumansdisease predictionGeneticsmachine learningmicrobiome

Identifiers

PMID39871711
PMCPMC11956383

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.