Evidence map›Paper›PMID 38838550›Full record

ReviewCurrent opinion in virology2024

Role of mucosal-associated invariant T cells in coronavirus disease 2019 vaccine immunogenicity.

Ali Amini, Paul Klenerman, Nicholas M Provine

Abstract readReview
In one paragraph

Review in Current opinion in virology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
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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

3 authors.

Ali AminiTranslational Gastroenterology Unit, Nuffield Department of Medicine - Experimental Medicine, University of Oxford, UK.
Paul KlenermanTranslational Gastroenterology Unit, Nuffield Department of Medicine - Experimental Medicine, University of Oxford, UK; Peter Medawar Building for Pathogen Research, Nuffield Department of Medicine, University of Oxford, UK; Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, UK. Electronic address: paul.klenerman@ndm.ox.ac.uk.
Nicholas M ProvinePandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, UK; Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, UK. Electronic address: nicholas.provine@ndm.ox.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mucosal-associated invariant T (MAIT) cells are an unconventional T cell population that are highly abundant in humans. They possess a semi-invariant T cell receptor (TCR) that recognises microbial metabolites formed during riboflavin biosynthesis, presented on a nonpolymorphic MHC-like molecule MR1. MAIT cells possess an array of effector functions, including type 1, type 17, and tissue repair activity. Deployment of these functions depends on the stimuli they receive through their TCR and/or cytokine receptors. Strong cytokine signalling, such as in response to vaccination, can bypass TCR triggering and provokes a strong proinflammatory response. Although data are still emerging, multiple aspects of MAIT cell biology are associated with modulation of immunity induced by the coronavirus disease 2019 mRNA and adenovirus vector vaccines. In this review, we will address how MAIT cells may play a role in immunogenicity of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and how these cells can be harnessed as cellular adjuvants.

Indexed as

COVID-19COVID-19 VaccinesMucosal-Associated Invariant T CellsSARS-CoV-2AnimalsCytokinesHumansImmunogenicity, VaccineReceptors, Antigen, T-CellCOVID-19 VaccinesCytokinesReceptors, Antigen, T-Cell

Identifiers

PMID38838550
PMCPMC11511680

What OpenQuestion holds

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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.