Evidence map›Paper›PMID 41087573›Full record

ArticleScientific reports2025

Surface electrical impedance myography detects disease in an adult-onset SOD1-G93A zebrafish model of amyotrophic lateral sclerosis.

Seward B Rutkove, Priyansh Shah, Laura Hevenor, Gaurav Tiwari, Dhrumil Patil, Tyler Mourey, Janice A Nagy, Anjali K Nath

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. ALS: a field in motion.Scientific reports · 2025
    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

8 authors.

Seward B RutkoveDepartment of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA. srutkove@bidmc.harvard.edu.
Priyansh ShahHarvard Medical School, Boston, MA, 02115, USA.
Laura HevenorDepartment of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA.
Gaurav TiwariDepartment of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA.
Dhrumil PatilHarvard Medical School, Boston, MA, 02115, USA.
Tyler MoureyZebrafish Core Facility, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.
Janice A NagyDepartment of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA.
Anjali K NathHarvard Medical School, Boston, MA, 02115, USA. anath1@bidmc.harvard.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that is characterized by loss of motor neurons and atrophy of skeletal muscle. Current FDA-approved drugs to treat ALS are only modestly effective at slowing the progression of the disease. Rodents have been the standard preclinical animal model for testing candidate ALS drugs; however, alternative animal models, including zebrafish, are being studied to accelerate therapeutic discovery. Here, we sought to advance a model of ALS in zebrafish with associated tools to serve as biomarkers of neuromuscular deterioration. Thus, we applied noninvasive, surface electrical impedance myography (EIM) methodology to SOD1

Indexed as

Amyotrophic Lateral SclerosisMyographySuperoxide Dismutase-1AnimalsDisease Models, AnimalElectric ImpedanceHumansMotor NeuronsMuscle, SkeletalZebrafishSuperoxide Dismutase-1Amyotrophic lateral sclerosisBiomarkersElectrical impedance myographyFast twitch muscle fibersNeuromuscular diseasePreclinical animal modelsReliability measuresSkeletal muscleSkeletal muscle atrophySlow twitch muscle fibersZebrafish

Identifiers

PMID41087573
PMCPMC12521378

What OpenQuestion holds

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LicenceCC BY
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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.