Evidence map›Paper›PMID 38002264›Full record

ReviewBiomolecules2023

Muscle Involvement in Amyotrophic Lateral Sclerosis: Understanding the Pathogenesis and Advancing Therapeutics.

Elisa Duranti, Chiara Villa

Open access · goldAbstract readReview
In one paragraph

Review in Biomolecules, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
5.1field-weighted citation impact, top 4% 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

21 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review of the Pathology of Muscle in Amyotrophic Lateral Sclerosis.International journal of molecular sciences · 2026
    Review
  5. Article
  6. Article
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  19. Article
  20. Edaravone: A Novel Possible Drug for Cancer Treatment?International journal of molecular sciences · 2024
    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 at 1 institution in 1 country.

Elisa DurantiSchool of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.ORCID 0000-0003-0631-3676
Chiara VillaSchool of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy.ORCID 0000-0003-2730-5974
University of Milano-Bicocca · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal condition characterized by the selective loss of motor neurons in the motor cortex, brainstem, and spinal cord. Muscle involvement, muscle atrophy, and subsequent paralysis are among the main features of this disease, which is defined as a neuromuscular disorder. ALS is a persistently progressive disease, and as motor neurons continue to degenerate, individuals with ALS experience a gradual decline in their ability to perform daily activities. Ultimately, muscle function loss may result in paralysis, presenting significant challenges in mobility, communication, and self-care. While the majority of ALS research has traditionally focused on pathogenic pathways in the central nervous system, there has been a great interest in muscle research. These studies were carried out on patients and animal models in order to better understand the molecular mechanisms involved and to develop therapies aimed at improving muscle function. This review summarizes the features of ALS and discusses the role of muscle, as well as examines recent studies in the development of treatments.

Indexed as

Amyotrophic Lateral SclerosisAnimalsHumansMotor NeuronsMuscle, SkeletalMuscular AtrophyParalysisamyotrophic lateral sclerosismuscle atrophyskeletal muscle

Identifiers

PMID38002264
PMCPMC10669302
OpenAlexW4387975260

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.