Evidence map›Paper›PMID 35328114›Full record

ArticleDiagnostics (Basel, Switzerland)2022

A User-Friendly Approach for Routine Histopathological and Morphometric Analysis of Skeletal Muscle Using CellProfiler Software.

Valerio Laghi, Valentina Ricci, Francesca De Santa, Alessio Torcinaro

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.2field-weighted citation impact, top 23% 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

8 citing papers in PubMed, 15 citations in OpenAlex.

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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 2 institutions in 2 countries.

Valerio LaghiUnité Macrophages et Développement de l'Immunité, Institut Pasteur, CNRS UMR3738, 75015 Paris, France.ORCID 0000-0003-0726-073X
Valentina RicciInstitute of Biochemistry and Cell Biology (IBBC), National Research Council of Italy (CNR), Via Ercole Ramarini, 32, Monterotondo, 00015 Rome, Italy.ORCID 0000-0001-9671-2659
Francesca De SantaInstitute of Biochemistry and Cell Biology (IBBC), National Research Council of Italy (CNR), Via Ercole Ramarini, 32, Monterotondo, 00015 Rome, Italy.ORCID 0000-0002-0034-5450
Alessio TorcinaroInstitute of Biochemistry and Cell Biology (IBBC), National Research Council of Italy (CNR), Via Ercole Ramarini, 32, Monterotondo, 00015 Rome, Italy.ORCID 0000-0002-9436-8783
National Research Council · ITCentre National de la Recherche Scientifique · FR

Funding

French Muscular Dystrophy Association 22613
6 · The paper itself

Abstract

Adult skeletal muscle is capable of active and efficient differentiation in the event of injury in both physiological and pathological conditions, such as in Duchenne muscular dystrophy (DMD). DMD is characterized by different features, such as continuous cycles of degeneration/regeneration, fiber heterogeneity, chronic inflammation and fibrosis. A well-defined and standardized approach for histological and morphometric analysis of muscle samples is necessary in order to measure and quantify specific regenerative parameters in myopathies. Indeed, non-automatic methods are time-consuming and prone to error. Here, we describe a simple automatized computational approach to quantify muscle parameters with specific pipelines to be run by CellProfiler software in an open-source and well-defined fashion. Our pipelines consist of running image-processing modules in CellProfiler with the aim of quantifying different histopathological muscle hallmarks in

Indexed as

cell localizationDuchenne muscular dystrophyhistologyhistopathological analysismyopathiesquantitative analysisskeletal muscleskeletal muscle differentiation

Identifiers

PMID35328114
PMCPMC8947111
OpenAlexW4214631969

What OpenQuestion holds

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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.