Evidence map›Paper›PMID 31998309›Full record

ReviewFrontiers in immunology2019

Non-Genetically Improving the Natural Cytotoxicity of Natural Killer (NK) Cells.

Martin Villalba, Catherine Alexia, Anais Bellin-Robert, Alexis Fayd'herbe de Maudave, Delphine Gitenay

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in immunology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
1.5field-weighted citation impact, top 17% 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

17 citing papers in PubMed, 25 citations in OpenAlex.

  1. Review
  2. Review
  3. Generation of non-genetically modified, CAR-like, NK cells.Journal for immunotherapy of cancer · 2024
    Article
  4. Oncoimmunology · 2024
    Article
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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

5 authors at 2 institutions in 1 country.

Martin VillalbaIRMB, Univ Montpellier, INSERM, CHU Montpellier, CNRS, Montpellier, France.
Catherine AlexiaIRMB, Univ Montpellier, INSERM, CHU Montpellier, Montpellier, France.
Anais Bellin-RobertIRMB, Univ Montpellier, INSERM, CHU Montpellier, Montpellier, France.
Alexis Fayd'herbe de MaudaveIRMB, Univ Montpellier, INSERM, CHU Montpellier, Montpellier, France.
Delphine GitenayIRMB, Univ Montpellier, INSERM, CHU Montpellier, Montpellier, France.
Université de Montpellier · FRInserm · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The innate lymphocyte lineage natural killer (NK) is now the target of multiple clinical applications, although none has received an agreement from any regulatory agency yet. Transplant of naïve NK cells has not proven efficient enough in the vast majority of clinical trials. Hence, new protocols wish to improve their medical use by producing them from stem cells and/or modifying them by genetic engineering. These techniques have given interesting results but these improvements often hide that natural killers are mainly that: natural. We discuss here different ways to take advantage of NK physiology to improve their clinical activity without the need of additional modifications except for

Indexed as

Cytotoxicity, ImmunologicAntibodies, MonoclonalAntibody-Dependent Cell CytotoxicityBiomarkersCellular MicroenvironmentClonal EvolutionCytokinesHumansImmunologic MemoryKiller Cells, NaturalLeukocyte Common AntigensLymphocyte ActivationAntibodies, MonoclonalBiomarkersCytokinesLeukocyte Common Antigensantibody-dependent cell cytotoxicity (ADCC)autoimmune diseasesCD45RAROmicroenvironmentmonoclonal antibodies (mAbs)NK cells

Identifiers

PMID31998309
PMCPMC6970430
OpenAlexW2995413550

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.