Evidence map›Paper›PMID 29140572›Full record

ReviewImmunology2018

Dissecting human ILC heterogeneity: more than just three subsets.

Yannick Simoni, Evan W Newell

Open access · bronzeAbstract readReview
In one paragraph

Review in Immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed
2.8field-weighted citation impact, top 9% 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

48 citing papers in PubMed, 70 citations in OpenAlex.

  1. Review
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  11. Impact of Organ Donor Pretreatment With Anti-Thymocyte Globulin in a Murine Model of Allogenic Kidney Transplantation.Transplant international : official journal of the European Society for Organ Transplantation · 2024
    Article
  12. Review
  13. Review
  14. The microbiota and the gut-liver axis in primary sclerosing cholangitis.Nature reviews. Gastroenterology & hepatology · 2023
    Review
  15. Review
  16. ILC3: a case of conflicted identity.Frontiers in immunology · 2023
    Review
  17. Article
  18. Review
  19. Article
  20. 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

2 authors at 1 institution in 1 country.

Yannick SimoniAgency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.ORCID 0000-0001-8425-7302
Evan W NewellAgency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN), Singapore.ORCID 0000-0002-2889-243X
Agency for Science, Technology and Research · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Innate lymphoid cells (ILCs) have been divided into three distinct groups based on functional capacities, cytokine profiles and transcription factor expression. Studies performed mainly in mice have demonstrated the importance of ILCs in chronic inflammation, infection, allergy and cancer. In this review, we discuss the heterogeneity of human ILC and focus primarily on the taxonomy of human ILC cell subsets and their phenotypical and functional diversity. We summarize recent findings concerning the diversity of ILCs between and within the major subsets [natural killer (NK), ILC1, intra-epithelial ILC1 (ieILC1), ILC2, ILC3, lymphoid tissues inducer (LTi) and ILC progenitor (ILCP)], as well as the abundance of each in human tissues. We also discuss the similarities observed between groups of cells in term of receptors expressed and cytokines produced, and how these relate to the pleiotropic properties of each subset.

Indexed as

AnimalsCytokinesHumansInflammationLymphocytesLymphocyte SubsetsNeoplasmsCytokinesheterogeneityhumanieILC1ILC1ILC2ILC3

Identifiers

PMID29140572
PMCPMC5795188
OpenAlexW2768801250

What OpenQuestion holds

Textmetadata
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.