ReviewFrontiers in cellular and infection microbiology2025
The role of long non-coding RNAs and circular RNAs in immune evasion of influenza A virus: recent advances.
Review in Frontiers in cellular and infection microbiology, 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.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The capacity of influenza A virus (IAV) to circumvent immune defenses in hosts renders it a persistent major peril to worldwide public health. Investigations conducted lately underscore the vital functions played by circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs) during interactions between viruses and their hosts. This review summarizes current understanding of how lncRNAs and circRNAs participate in IAV immune evasion by regulating antiviral signaling pathways, interfering with interferon responses, modulating inflammatory cytokine production and cell metabolism, and affecting viral replication. This review examines molecular actions exhibited by particular lncRNAs and circRNAs within such interactions, assesses their suitability for therapeutic targets and diagnostic biomarkers, and outlines avenues for subsequent investigations across this fast-developing domain.
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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.