Evidence map›Paper›PMID 40552465›Full record

ReviewBiochemical Society transactions2025

How do different cell populations orchestrate myelin regeneration?

Sara Grassi, Alessandro Prinetti

Abstract readReview
In one paragraph

Review in Biochemical Society transactions, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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.

Sara GrassiDepartment of Medical Biotechnology and Translational Medicine, Via Fratelli Cervi 93, 20090 Segrate (Milano),University of Milan, Milan, Italy.ORCID 0000-0002-9118-9982
Alessandro PrinettiDepartment of Medical Biotechnology and Translational Medicine, Via Fratelli Cervi 93, 20090 Segrate (Milano),University of Milan, Milan, Italy.ORCID 0000-0003-0252-2593

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Approximately 35 in 100,000 people are affected by diseases associated with loss of myelin, generally described as demyelinating diseases. Demyelinating diseases encompass many different pathological conditions characterized by heterogeneous and sometimes disease-specific etiopathological mechanisms. While several approaches aimed at ameliorating the symptoms and the progression of some of these diseases exist, the most effective cure for all demyelinating diseases would be regeneration of lost myelin. Myelin regeneration occurs spontaneously in the central nervous system in response to myelin damage but is inefficient for a variety of reasons, especially in human patients. In this review, we will discuss the contributions of different cell populations to the creation of conditions permissive for effective remyelination and to the formation of new myelin after injury. Moreover, we would like to highlight the importance of sphingolipids in the network of interactions between these cell populations. Mutations in genes encoding sphingolipid metabolic enzymes (such as GALC) represent a major risk factor for multiple sclerosis, and alterations in sphingolipid metabolism in specific cell types contribute to myelin damage. On the other hand, sphingolipid signaling, in particular through sphingosine 1 phosphate, directly affects the process of myelin regeneration, with distinct effects on different cellular populations.

Indexed as

Myelin SheathNerve RegenerationRegenerationAnimalsDemyelinating DiseasesHumansMultiple SclerosisRemyelinationSignal TransductionSphingolipidsSphingosineSphingolipidsSphingosinemultiple sclerosisremyelinationrHIgM22sphingolipidssphingosine 1-phosphate

Identifiers

PMID40552465
PMCPMC12312399

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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.