ArticleScientific reports2025
Impact of COVID-19 infection on Kawasaki disease and immune status in children.
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.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- Construction and validation of a diagnostic model for Kawasaki disease based on neutrophil-related genes and analysis of immune infiltration.Translational pediatrics · 2026Article
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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Patients infected with SARS-CoV-2 may experience acute and long-term immune disorders. Immunological factors are thought to play an important role in Kawasaki disease. To analyze the impact of COVID-19 infection on Kawasaki disease, this study retrospectively analyzed 161 children with Kawasaki disease onset during the COVID-19 pandemic. The proportion of IVIG-Resistant individuals and the rate of corticosteroid use in the 1-7 weeks from COVID-19 infection to Kawasaki disease onset were higher than that of the noninfected group, even after excluding suspected cases of multiple system inflammatory syndrome. Compared to the noninfected group, the level of CD4 was lower, and the levels of CD3
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Registered trials
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.