Evidence map›Paper›PMID 40074984›Full record

ReviewCellular and molecular life sciences : CMLS2025

Anti-interferon armamentarium of human coronaviruses.

Oyahida Khatun, Sumandeep Kaur, Shashank Tripathi

Abstract readReview
In one paragraph

Review in Cellular and molecular life sciences : CMLS, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Oyahida KhatunEmerging Viral Pathogens Laboratory, Centre for Infectious Disease Research, Indian Institute of Science, Bengaluru, India.
Sumandeep KaurEmerging Viral Pathogens Laboratory, Centre for Infectious Disease Research, Indian Institute of Science, Bengaluru, India.
Shashank TripathiEmerging Viral Pathogens Laboratory, Centre for Infectious Disease Research, Indian Institute of Science, Bengaluru, India. shashankt@iisc.ac.in.ORCID http://orcid.org/0000-0002-5702-9248

Funding

Blockchain for Impact Blockchain for Impact
6 · The paper itself

Abstract

Cellular innate immune pathways are formidable barriers against viral invasion, creating an environment unfavorable for virus replication. Interferons (IFNs) play a crucial role in driving and regulating these cell-intrinsic innate antiviral mechanisms through the action of interferon-stimulated genes (ISGs). The host IFN response obstructs viral replication at every stage, prompting viruses to evolve various strategies to counteract or evade this response. Understanding the interplay between viral proteins and cell-intrinsic IFN-mediated immune mechanisms is essential for developing antiviral and anti-inflammatory strategies. Human coronaviruses (HCoVs), including SARS-CoV-2, MERS-CoV, SARS-CoV, and seasonal coronaviruses, encode a range of proteins that, through shared and distinct mechanisms, inhibit IFN-mediated innate immune responses. Compounding the issue, a dysregulated early IFN response can lead to a hyper-inflammatory immune reaction later in the infection, resulting in severe disease. This review provides a brief overview of HCoV replication and a detailed account of its interaction with host cellular innate immune pathways regulated by IFN.

Indexed as

Antiviral AgentsCoronavirusCoronavirus InfectionsInterferonsHost-Pathogen InteractionsHumansImmunity, InnateMiddle East Respiratory Syndrome CoronavirusSARS-CoV-2Virus ReplicationAntiviral AgentsInterferonsHuman coronavirusesImmune dysregulationInnate immunityInterferonInterferon-stimulated genesViral proteins

Identifiers

PMID40074984
PMCPMC11904029

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.