Evidence map›Paper›PMID 32488324›Full record

ArticleEuropean radiology experimental2020

Modelling the skeletal muscle injury recovery using in vivo contrast-enhanced micro-CT: a proof-of-concept study in a rat model.

Bruno Paun, Daniel García Leon, Alex Claveria Cabello, Roso Mares Pages, Elena de la Calle Vargas, Paola Contreras Muñoz, Vanessa Venegas Garcia, Joan Castell-Conesa, Mario Marotta Baleriola, Jose Raul Herance Camacho

Erratum issuedAbstract read
In one paragraph

Article in European radiology experimental, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Bruno PaunMedical Molecular Imaging Group, Vall d'Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona (UAB), Passeig de la Vall d'Hebron 119-129, 08035, Barcelona, Spain.
Daniel García LeonMedical Molecular Imaging Group, Vall d'Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona (UAB), Passeig de la Vall d'Hebron 119-129, 08035, Barcelona, Spain.
Alex Claveria CabelloMedical Molecular Imaging Group, Vall d'Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona (UAB), Passeig de la Vall d'Hebron 119-129, 08035, Barcelona, Spain.
Roso Mares PagesMedical Molecular Imaging Group, Vall d'Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona (UAB), Passeig de la Vall d'Hebron 119-129, 08035, Barcelona, Spain.
Elena de la Calle VargasMedical Molecular Imaging Group, Vall d'Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona (UAB), Passeig de la Vall d'Hebron 119-129, 08035, Barcelona, Spain.
Paola Contreras MuñozHealth & Biomedicine division, Leitat Technological Center, 2. C/ Pallars, 179-185, 08005, Barcelona, Spain.
Vanessa Venegas GarciaHealth & Biomedicine division, Leitat Technological Center, 2. C/ Pallars, 179-185, 08005, Barcelona, Spain.
Joan Castell-ConesaMedical Molecular Imaging Group, Vall d'Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona (UAB), Passeig de la Vall d'Hebron 119-129, 08035, Barcelona, Spain.
Mario Marotta BaleriolaHealth & Biomedicine division, Leitat Technological Center, 2. C/ Pallars, 179-185, 08005, Barcelona, Spain.
Jose Raul Herance CamachoMedical Molecular Imaging Group, Vall d'Hebron Research Institute (VHIR), CIBER-BBN, CIBBIM-Nanomedicine, ISCIII, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona (UAB), Passeig de la Vall d'Hebron 119-129, 08035, Barcelona, Spain. raul.herance@vhir.org.

Funding

Agència de Gestió d'Ajuts Universitaris i de Recerca 2017SGR1303Horizon 2020 Framework Programme 761031Instituto de Salud Carlos III PI16/02064
6 · The paper itself

Abstract

backgroundSkeletal muscle injury characterisation during healing supports trauma prognosis. Given the potential interest of computed tomography (CT) in muscle diseases and lack of in vivo CT methodology to image skeletal muscle wound healing, we tracked skeletal muscle injury recovery using in vivo micro-CT in a rat model to obtain a predictive model.

methodsSkeletal muscle injury was performed in 23 rats. Twenty animals were sorted into five groups to image lesion recovery at 2, 4, 7, 10, or 14 days after injury using contrast-enhanced micro-CT. Injury volumes were quantified using a semiautomatic image processing, and these values were used to build a prediction model. The remaining 3 rats were imaged at all monitoring time points as validation. Predictions were compared with Bland-Altman analysis.

resultsOptimal contrast agent dose was found to be 20 mL/kg injected at 400 μL/min. Injury volumes showed a decreasing tendency from day 0 (32.3 ± 12.0mm

conclusionContrast-enhanced CT allowed in vivo tracking of skeletal muscle injury recovery in rat.

Indexed as

Wound HealingAnimalsContrast MediaDisease Models, AnimalImage Processing, Computer-AssistedIopamidolMuscle, SkeletalProof of Concept StudyRatsRats, WistarX-Ray MicrotomographyContrast MediaIopamidolMuscle (skeletal)Muscular diseasesRatsTomography (x-ray computed)Wound healing

Identifiers

PMID32488324
PMCPMC7266881

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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.