Evidence map›Paper›PMID 41120352›Full record

ArticleScientific reports2025

Multiphoton microscopy and tissue clearing for 3D characterization of the vasculature and fibrosis remodeling in rat dystrophic skeletal muscle.

Ibrahim Hassani, Mireille Ledevin, Chantal Thorin, Tony Fiore, Marie-Anne Colle, Karl Rouger, Laurence Dubreil

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

7 authors.

Ibrahim HassaniOniris, INRAE, PAnTher, Nantes, 44300, France.
Mireille LedevinOniris, INRAE, PAnTher, Nantes, 44300, France.
Chantal ThorinOniris, INRAE, PAnTher, Nantes, 44300, France.
Tony FioreNikon Healthcare France, succursale Nikon Europe B.V., 94500, Champigny-sur-Marne, France.
Marie-Anne ColleOniris, INRAE, PAnTher, Nantes, 44300, France.
Karl Rouger *Oniris, INRAE, PAnTher, Nantes, 44300, France. karl.rouger@inrae.fr.
Laurence Dubreil *Oniris, INRAE, PAnTher, Nantes, 44300, France. laurence.dubreil@inrae.fr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Duchenne muscular dystrophy (DMD) is characterized by progressive muscle fiber degeneration and replacement by fibrous and adipose tissues, alongside significant vascular abnormalities. Traditional two-dimensional (2D) histological assessments provide limited insight into the complex three-dimensional (3D) spatial organization and structural disorganization within dystrophic muscle. Here, we present a novel 3D approach combining multiphoton microscopy (second harmonic generation [SHG] and two-photon excited fluorescence [TPEF]) with tissue-clearing methods to comprehensively characterize microvascular and connective tissue remodeling in dystrophic skeletal muscle. We established a dedicated 3D image analysis workflow utilizing deep-learning-based segmentation techniques to quantify key parameters in both vascular and fibrotic compartments in healthy and dystrophic rat muscle samples. Our findings reveal a profound spatial reorganization of the vascular network in dystrophic muscle, marked by its embedding within an expanded connective tissue and a significant reduction in physical interactions with muscle fibers. This advanced imaging and analysis pipeline provides detailed insights into the extent of vascular and fibrotic remodeling in dystrophic muscle, and represents a powerful tool for monitoring disease progression and evaluating the efficacy of therapeutic interventions.

Indexed as

Imaging, Three-DimensionalMicroscopy, Fluorescence, MultiphotonMuscle, SkeletalMuscular Dystrophy, DuchenneAnimalsDisease Models, AnimalFibrosisMaleRatsClearingFibrosisImage analysisMultiphoton microscopyMuscular dystrophyVasculature

Identifiers

PMID41120352
PMCPMC12540727

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Registered trials

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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.