Evidence map›Paper›PMID 33139995›Full record

ArticleQuantitative imaging in medicine and surgery2020

Non-invasive assessment of early and acute myocarditis in a rat model using cardiac magnetic resonance tissue tracking analysis of myocardial strain.

Jing Zhu, Yushu Chen, Ziqian Xu, Shiyu Wang, Lei Wang, Xiaoxin Liu, Fabao Gao

Open access · diamondAbstract read
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Article in Quantitative imaging in medicine and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.5field-weighted citation impact, top 15% 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

6 citing papers in PubMed, 14 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

7 authors at 2 institutions in 1 country.

Jing ZhuDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Yushu ChenDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Ziqian XuDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Shiyu WangDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Lei WangDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Xiaoxin LiuDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Fabao GaoDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, China.
West China Hospital of Sichuan University · CNSichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMyocardial strain analysis can provide diagnostic and prognostic information for myocarditis. The aim of the present study was to assess early and acute myocarditis in a rat model using cardiac magnetic resonance tissue tracking (CMR-TT) for myocardial strain analysis. We compared the strain's findings with the histological and immunohistochemical results.

methodsExperimental autoimmune myocarditis (EAM) was induced by footpad injections of porcine cardiac myosin. The rats were examined by 7.0T preclinical CMR at day 14 (n=15) and day 21 (n=16) after EAM induction and the two control groups (each n=15) were also examined at day 14 and day 21, respectively. Using CMR-TT, we found a global peak systolic radial strain (Err

resultsEll

conclusionsThe CMR-TT strain analysis allowed accurate and reliable evaluation of early and acute myocarditis in a rat model, and has the potential to serve as a diagnostic indicator for the assessment of myocardial dysfunction in myocarditis.

Indexed as

Cardiac magnetic resonanceexperimental autoimmune myocarditismyocardial strain analysismyocarditistissue tracking

Identifiers

PMID33139995
PMCPMC7547264
OpenAlexW3083363318

What OpenQuestion holds

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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.