Evidence map›Paper›PMID 42068023›Full record

ArticleJournal of anatomy2026

Scratching beneath the surface: Quantification of muscle architecture and myosin heavy chain content in the forelimbs of black-tailed prairie dogs (Cynomys, Rodentia).

Luke A Scheetz, Carter A Burnett, Benjamin R Kimble, David A Eads, Michael T Butcher

Abstract read
In one paragraph

Article in Journal of anatomy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Luke A ScheetzDepartment of Chemical and Biological Sciences, Youngstown State University, Youngstown, Ohio, USA.
Carter A BurnettDepartment of Chemical and Biological Sciences, Youngstown State University, Youngstown, Ohio, USA.
Benjamin R KimbleDepartment of Chemical and Biological Sciences, Youngstown State University, Youngstown, Ohio, USA.
David A EadsEads Ecological Services LLC, Longmont, Colorado, USA.
Michael T ButcherDepartment of Chemical and Biological Sciences, Youngstown State University, Youngstown, Ohio, USA.ORCID https://orcid.org/0000-0001-7323-5132

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Black-tailed prairie dogs (Cynomys ludovicianus) excavate burrow systems for a variety of reasons, including social organization and predator avoidance. As such, they have evolved a suite of musculoskeletal traits in their forelimbs that are employed for scratch-digging (i.e., digging motion performed by alternating power and recovery strokes). However, the degree to which their muscular anatomy has become modified in response to the selective pressures of their semi-fossorial lifestyle is unknown. To better understand the functional capacities of their forelimb musculature, dissections of C. ludovicianus (N = 9) were conducted to quantify limb mechanical advantage, muscle architectural properties, and myosin heavy chain (MHC) isoform content. Compared with previous data from other ground squirrels, forelimb muscle mass distribution in black-tailed prairie dogs is broadly similar with a large investment of scapular and shoulder muscle mass that accounts for two-thirds of the total forelimb muscle mass. The majority of muscles have long fascicles with correspondingly high fascicle length-to-muscle length (L

Indexed as

ForelimbMuscle, SkeletalMyosin Heavy ChainsSciuridaeAnimalsBiomechanical PhenomenaFemaleMaleMyosin Heavy ChainsC. LudovicianuscontractilefascicleforcemassMHCPCSAtorque

Identifiers

PMID42068023
PMCPMC13398623

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.