Evidence map›Paper›PMID 38299028›Full record

ArticleiScience2024

Predicted risk of heart failure pandemic due to persistent SARS-CoV-2 infection using a three-dimensional cardiac model.

Kozue Murata, Akiko Makino, Keizo Tomonaga, Hidetoshi Masumoto

Open access · goldAbstract read
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
3.3field-weighted citation impact, top 6% 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

5 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 1 institution in 1 country.

Kozue MurataClinical Translational Research Program, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
Akiko MakinoLaboratory of RNA Viruses, Department of Virus Research, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan.
Keizo TomonagaLaboratory of RNA Viruses, Department of Virus Research, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan.
Hidetoshi MasumotoClinical Translational Research Program, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
Kyoto University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients with chronic cardiomyopathy may have persistent viral infections in their hearts, particularly with SARS-CoV-2, which targets the ACE2 receptor highly expressed in human hearts. This raises concerns about a potential global heart failure pandemic stemming from COVID-19, an SARS-CoV-2 pandemic in near future. Although faced with this healthcare caveat, there is limited research on persistent viral heart infections, and no models have been established. In this study, we created an SARS-CoV-2 persistent infection model using human iPS cell-derived cardiac microtissues (CMTs). Mild infections sustained viral presence without significant dysfunction for a month, indicating persistent infection. However, when exposed to hypoxic conditions mimicking ischemic heart diseases, cardiac function deteriorated alongside intracellular SARS-CoV-2 reactivation in cardiomyocytes and disrupted vascular network formation. This study demonstrates that SARS-CoV-2 persistently infects the heart opportunistically causing cardiac dysfunction triggered by detrimental stimuli such as ischemia, potentially predicting a post COVID-19 era heart failure pandemic.

Indexed as

Biological sciencesBiology experimental methodsCell biologyHealth sciencesHealth technologyMedicineNatural sciencesStem cells research

Identifiers

PMID38299028
PMCPMC10829886
OpenAlexW4390116231

What OpenQuestion holds

Textmetadata
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.