Evidence map›Paper›PMID 38367020›Full record

ReviewThe journals of gerontology. Series A, Biological sciences and medical sciences2025

Skeletal Muscle Aging: Lessons From Teleosts.

Tuyen K Quach, Megan F Taylor, Peter D Currie, Nir Eynon, Avnika A Ruparelia

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The journals of gerontology. Series A, Biological sciences and medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.1field-weighted citation impact, top 16% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  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

5 authors at 2 institutions in 1 country.

Tuyen K QuachCentre for Muscle Research, Department of Anatomy and Physiology, School of Biomedical Sciences, Faculty of Medicine, Dentistry, and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia.
Megan F TaylorAustralian Regenerative Medicine Institute, Monash University, Clayton, Victoria, Australia.
Peter D CurrieAustralian Regenerative Medicine Institute, Monash University, Clayton, Victoria, Australia.
Nir EynonAustralian Regenerative Medicine Institute, Monash University, Clayton, Victoria, Australia.ORCID 0000-0003-4046-8276
Avnika A RupareliaCentre for Muscle Research, Department of Anatomy and Physiology, School of Biomedical Sciences, Faculty of Medicine, Dentistry, and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0002-1012-0079
Australian Regenerative Medicine Institute · AUThe University of Melbourne · AU

Funding

Medical Research Future Fund
6 · The paper itself

Abstract

Aging is the greatest risk factor for a multitude of age-related diseases including sarcopenia-the loss of skeletal muscle mass and strength-which occurs at remarkable rates each year. There is an unmet need not only to understand the mechanisms that drive sarcopenia but also to identify novel therapeutic strategies. Given the ease and affordability of husbandry, along with advances in genomics, genome editing technologies, and imaging capabilities, teleost models are increasingly used for aging and sarcopenia research. Here, we explain how teleost species such as zebrafish, African turquoise killifish, and medaka recapitulate many of the classical hallmarks of sarcopenia, and discuss the various dietary, pharmacological, and genetic approaches that have been used in teleosts to understand the mechanistic basis of sarcopenia.

Indexed as

AgingFishesMuscle, SkeletalSarcopeniaAnimalsDisease Models, AnimalHumansZebrafishAge-related pathologyFish modelsSarcopeniaSkeletal muscle

Identifiers

PMID38367020
PMCPMC12080710
OpenAlexW4391919089

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.