Evidence map›Paper›PMID 41873181›Full record

ReviewAnnals of laboratory medicine2026

Natural Killer Cell Assays: Clinical Applications and Future Directions.

Minjeong Nam, Wooseok Park, Hyun-Young Kim, Duck Cho

Abstract readReview
In one paragraph

Review in Annals of laboratory medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Minjeong NamDepartment of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-3542-3487
Wooseok ParkDepartment of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0009-0002-3902-0968
Hyun-Young KimDepartment of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-0553-7096
Duck ChoDepartment of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-6861-3282

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Natural killer (NK) cells play critical roles in immune surveillance and homeostasis maintenance through cytotoxicity and cytokine release. The ability of NK cells to eliminate target cells without the need for prior antigen recognition or antibody involvement has drawn considerable attention for translational and clinical applications. As clinical applications continue to broaden, interest in NK cell assays has been growing markedly. This review provides an in-depth discussion of current clinical applications of NK cell assays, including NK cell phenotype, frequency, and functional activity assessments. This review further highlights the diagnostic potential of these assays in terms of hemophagocytic lymphohistiocytosis and potential NK cell biomarker candidates in diverse pathological contexts, such as cancer, infection, autoimmune diseases, and other diseases. By integrating clinical insights with technological advancements, NK cell assays can serve as valuable tools for disease diagnosis, prognosis, and therapeutic monitoring.

Indexed as

Killer Cells, NaturalAnimalsAutoimmune DiseasesBiomarkersHumansLymphohistiocytosis, HemophagocyticNeoplasmsBiomarkersAutoimmune diseasesCancerCytotoxicityFunctional assaysHemophagocytic lymphohistocytosisInfectionsNatural killer cells

Identifiers

PMID41873181
PMCPMC13071281

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.