Evidence map›Paper›PMID 29280974›Full record

ReviewInternational journal of molecular sciences2017

Harnessing Invariant NKT Cells to Improve Influenza Vaccines: A Pig Perspective.

Guan Yang, Jürgen A Richt, John P Driver

Open access · goldAbstract readReview
In one paragraph

Review in International journal of molecular sciences, 2017. 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
0.7field-weighted citation impact, top 31% 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

8 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Frontiers in immunology · 2023
    Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
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 at 2 institutions in 1 country.

Guan YangDepartment of Animal Sciences, University of Florida, Gainesville, FL 32611, USA. guan.yang@ufl.edu.
Jürgen A RichtCollege of Veterinary Medicine, Kansas State University, Manhattan, KS 66506, USA. jricht@vet.k-state.edu.
John P DriverDepartment of Animal Sciences, University of Florida, Gainesville, FL 32611, USA. jdriver@ufl.edu.ORCID 0000-0002-6714-3335
University of Florida · USKansas State University · US

Funding

Harnessing Natural Killer T cells to counteract swine influenzaR01HD092286 · NICHD · UNIVERSITY OF MISSOURI-COLUMBIA · PI DRIVER, JOHN · 2017 to 2021
$2.6M
NIAID NIH HHS HHSN266200700006CNICHD NIH HHS R01 HD092286
6 · The paper itself

Abstract

Invariant natural killer T (iNKT) cells are an "innate-like" T cell lineage that recognize glycolipid rather than peptide antigens by their semi-invariant T cell receptors. Because iNKT cells can stimulate an extensive array of immune responses, there is considerable interest in targeting these cells to enhance human vaccines against a wide range of microbial pathogens. However, long overlooked is the potential to harness iNKT cell antigens as vaccine adjuvants for domestic animal species that express the iNKT cell-CD1d system. In this review, we discuss the prospect of targeting porcine iNKT cells as a strategy to enhance the efficiency of swine influenza vaccines. In addition, we compare the phenotype and tissue distribution of porcine iNKT cells. Finally, we discuss the challenges that must be overcome before iNKT cell agonists can be contemplated for veterinary use in livestock.

Indexed as

AnimalsAntigens, CD1dImmunity, InnateInfluenza VaccinesNatural Killer T-CellsOrthomyxoviridae InfectionsSwineSwine DiseasesT-Lymphocytes, Helper-InducerAntigens, CD1dInfluenza VaccinesadjuvantsCD1dinfluenza vaccineiNKT cellspig

Identifiers

PMID29280974
PMCPMC5796018
OpenAlexW2779449475

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.