Evidence map›Paper›PMID 35043970›Full record

ReviewHistology and histopathology2022

History and development of staining methods for skeletal muscle fiber types.

Shoko Sawano, Wataru Mizunoya

Abstract readReview
PubMed Publisher
In one paragraph

Review in Histology and histopathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

16 citing papers in PubMed, 23 citations in OpenAlex.

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  16. Identification of Subproteomic Markers for Skeletal Muscle Profiling.Methods in molecular biology (Clifton, N.J.) · 2023
    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

2 authors at 1 institution in 1 country.

Shoko Sawano *Department of Food and Life Science, School of Life and Environmental Science, Azabu University, Sagamihara, Japan. sawano@azabu-u.ac.jp.
Wataru Mizunoya *Department of Animal Science and Biotechnology, School of Veterinary Medicine, Azabu University, Sagamihara, Japan. mizunoya.wataru.k91@kyoto-u.jp.
Azabu University · JP

Funding

Japan Society for the Promotion of Science (JSPS) KAKENHI 19H03109Japan Society for the Promotion of Science (JSPS) KAKENHI 21K05896
6 · The paper itself

Abstract

The contractile and metabolic properties of skeletal muscles depend on the composition of muscle fibers. There are two major fiber types: type 1 and type 2. Type 2 fibers are further subdivided into type 2A, 2X, and 2B fibers. Muscle fiber type composition is an important property that affects sports performance and metabolic ability in humans, and meat quality in domestic animals. In this review, we summarize the history of muscle fiber type classification based on various staining methods for skeletal muscle sections. The history illustrates the development of an experimental method to detect myosin heavy chain (MyHC) proteins, which are the most common marker molecules for muscle fiber type. Metabolic enzymes, such as nicotinamide adenine dinucleotide-tetrazolium reductase and succinate dehydrogenase are also described for histochemical staining combined with myosin ATPase staining. We found an improvement in the quality of antibodies used for immunostaining of MyHC, from polyclonal antibodies to monoclonal antibodies (mAbs) and then to mAbs produced by synthetic peptides as antigens. We believe that the information presented herein will assist researchers in selecting optimal staining methods, dependent on the experimental conditions and purposes.

Indexed as

Muscle Fibers, SkeletalMyosin Heavy ChainsAnimalsAntibodies, MonoclonalImmunohistochemistryMuscle, SkeletalMyosinsStaining and LabelingAntibodies, MonoclonalMyosin Heavy ChainsMyosins

Identifiers

PMID35043970
OpenAlexW4205538434

What OpenQuestion holds

Textmetadata
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.