Evidence map›Paper›PMID 36865342›Full record

ArticlebioRxiv : the preprint server for biology2023

Quantitative model of aging-related muscle degeneration: a Drosophila study.

Maria Chechenova, Hannah Stratton, Kaveh Kiani, Erik Gerberich, Alesia Alekseyenko, Natasya Tamba, SooBin An, Lizzet Castillo, Emily Czajkowski, Christina Talley and 1 more

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. 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, 3 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 2 institutions in 1 country.

Maria ChechenovaDepartment of Molecular and Cellular Biology, Kennesaw State University, Kennesaw, GA.
Hannah StrattonDepartment of Molecular and Cellular Biology, Kennesaw State University, Kennesaw, GA.
Kaveh KianiDepartment of Molecular and Cellular Biology, Kennesaw State University, Kennesaw, GA.
Erik GerberichDepartment of Molecular and Cellular Biology, Kennesaw State University, Kennesaw, GA.
Alesia AlekseyenkoDepartment of Molecular and Cellular Biology, Kennesaw State University, Kennesaw, GA.
Natasya TambaDepartment of Molecular and Cellular Biology, Kennesaw State University, Kennesaw, GA.
SooBin AnDepartment of Molecular and Cellular Biology, Kennesaw State University, Kennesaw, GA.
Lizzet CastilloDepartment of Biology, University of New Mexico, Albuquerque, NM.
Emily CzajkowskiDepartment of Biology, University of New Mexico, Albuquerque, NM.
Christina TalleyDepartment of Molecular and Cellular Biology, Kennesaw State University, Kennesaw, GA.
Anton BryantsevDepartment of Molecular and Cellular Biology, Kennesaw State University, Kennesaw, GA.
Kennesaw State University · USUniversity of New Mexico · US

Funding

Identifying Critical Molecular Targets to Suppress Age-Related Muscle Fiber LossR03AG048496 · NIA · UNIVERSITY OF NEW MEXICO · PI BRYANTSEV, ANTON · 2014 to 2015
$141k
NIA NIH HHS R03 AG048496
6 · The paper itself

Abstract

Changes in the composition and functionality of somatic muscles is a universal hallmark of aging that is displayed by a wide range of species. In humans, complications arising from muscle decline due to sarcopenia aggravate morbidity and mortality rates. The genetics of aging-related deterioration of muscle tissue is not well understood, which prompted us to characterize aging-related muscle degeneration in

Indexed as

agingdegenerationDrosophilamusclemuscle fibersarcopenia

Identifiers

PMID36865342
PMCPMC9980004
OpenAlexW4321435800

What OpenQuestion 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 OpenQuestion graph.