Evidence map›Paper›PMID 32165699›Full record

ArticleScientific reports2020

Synchrotron radiation imaging analysis of neural damage in mouse soleus muscle.

Jiwon Lee, Sang-Hun Jang, Suk-Jun Lee, Onseok Lee

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2020. 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
0.8field-weighted citation impact, top 31% 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, 13 citations in OpenAlex.

  1. Article
  2. 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

4 authors at 3 institutions in 1 country.

Jiwon LeeDepartment of Computer & Science Engineering, Graduate School, Soonchunhyang University, 22, Soonchunhyang-ro, Asan City, Chungnam, 31538, Republic of Korea.
Sang-Hun JangDepartment of Physical Therapy, College of Health and Life Science, Korea National University of Transportation, 61, Daehak-ro, Yonggang-ri, Jeungpyeong-eup, Jeungpyeong-gun, Chungcheongbuk-do, 27909, Republic of Korea.
Suk-Jun LeeDepartment of Biomedical Laboratory Science, College of Health & Medical Sciences, Cheongju University, 298, Daesung-ro, Cheongju City, Chungbuk, 28503, Republic of Korea.
Onseok LeeDepartment of Computer & Science Engineering, Graduate School, Soonchunhyang University, 22, Soonchunhyang-ro, Asan City, Chungnam, 31538, Republic of Korea. leeos@sch.ac.kr.ORCID http://orcid.org/0000-0002-3696-9353
Soonchunhyang University · KRCheongju University · KRKorea National University of Transportation · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Damage to lower limb muscles requires accurate analysis of the muscular condition via objective microscopic diagnosis. However, microscopic tissue analysis may cause deformation of the tissue structure due to injury induced by external factors during tissue sectioning. To substantiate these muscle injuries, we used synchrotron X-ray imaging technology to project extremely small objects, provide three-dimensional microstructural analysis as extracted samples. In this study, we used mice as experimental animals to create soleus muscle models with various nerve injuries. We morphologically analyzed and quantified the damaged Section and Crush muscles, respectively, via three-dimensional visualization using synchrotron radiation X-ray imaging to diagnose muscle injury. Results of this study can also be used as basic data in the medical imaging field.

Indexed as

AnimalsImaging, Three-DimensionalMaleMiceMuscle, SkeletalRadiographySciatic NerveSynchrotronsX-Rays

Identifiers

PMID32165699
PMCPMC7067770
OpenAlexW3010915835

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.