Evidence map›Paper›PMID 22051914›Full record

ReviewNature reviews. Neurology2011

Molecular pathways of motor neuron injury in amyotrophic lateral sclerosis.

Laura Ferraiuolo, Janine Kirby, Andrew J Grierson, Michael Sendtner, Pamela J Shaw

Open access · greenAbstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Neurology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 326 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
326citing papers in PubMed, 2 pooled it
32.4field-weighted citation impact, top 1% 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

326 citing papers in PubMed, 2 syntheses or guidelines pooled it, 607 citations in OpenAlex.

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266 more citing papers are in PubMed but not listed here.

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

5 authors at 2 institutions in 2 countries.

Laura FerraiuoloAcademic Neurology Unit, Sheffield Institute for Translational Neuroscience, Department of Neuroscience, School of Medicine and Biomedical Sciences, University of Sheffield, 385A Glossop Road, Sheffield S10 2HQ, UK.
Janine Kirby
Andrew J Grierson
Michael Sendtner
Pamela J Shaw
University of Sheffield · GBUniversity of Würzburg · DE

Funding

Medical Research CouncilWellcome Trust
6 · The paper itself

Abstract

Amyotrophic lateral sclerosis (ALS) is a genetically diverse disease. At least 15 ALS-associated gene loci have so far been identified, and the causative gene is known in approximately 30% of familial ALS cases. Less is known about the factors underlying the sporadic form of the disease. The molecular mechanisms of motor neuron degeneration are best understood in the subtype of disease caused by mutations in superoxide dismutase 1, with a current consensus that motor neuron injury is caused by a complex interplay between multiple pathogenic processes. A key recent finding is that mutated TAR DNA-binding protein 43 is a major constituent of the ubiquitinated protein inclusions in ALS, providing a possible link between the genetic mutation and the cellular pathology. New insights have also indicated the importance of dysregulated glial cell-motor neuron crosstalk, and have highlighted the vulnerability of the distal axonal compartment early in the disease course. In addition, recent studies have suggested that disordered RNA processing is likely to represent a major contributing factor to motor neuron disease. Ongoing research on the cellular pathways highlighted in this Review is predicted to open the door to new therapeutic interventions to slow disease progression in ALS.

Indexed as

Amyotrophic Lateral SclerosisHumansMotor NeuronsNerve Degeneration

Identifiers

PMID22051914
OpenAlexW2025413109

What OpenQuestion holds

Textmetadata
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.