Evidence map›Paper›PMID 35344177›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Assessing the Response of Human NK Cell Subsets to Infection by Clinically Isolated Virus Strains.

Nobuyo Yawata, Makoto Yawata

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. 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
0.6field-weighted citation impact, top 39% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

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

2 authors at 2 institutions in 2 countries.

Nobuyo YawataDepartment of Ocular Pathology and Imaging Science, Kyushu University, Fukuoka, Japan. yawata.nobuyo.718@m.kyushu-u.ac.jp.
Makoto YawataDepartment of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Kumamoto University · JPKyushu University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Natural killer (NK) cells play a critical role in defending against virus infections.Investigating human NK cell antiviral functions is of prime importance; however, there are challenges such as the human-specific nature of many viruses and differences in NK cell surface markers between humans and rodents. Research on the antivirus response of human NK cells must therefore be carefully planned around species tropism of the viruses of interest and the specific biological questions to be answered. The initial site of many virus infections is a mucosal/epithelial surface. In this context, a clinical virus infection at the ocular surface enables direct analyses on the mechanisms and consequences of infection and immune reactions in situ over the course of disease. For example, the site of infection of a clinical infection in the conjunctiva and cornea can be directly observed in real-time, utilizing split-lamp microscopy, and specimens are readily accessed with minimally invasive techniques.In this chapter, we describe protocols for investigating NK cell responses using clinically isolated viruses in co-culture assays. We also describe procedures for ex vivo analysis of conjunctiva-derived NK cells in adenovirus infection.

Indexed as

Virus DiseasesVirusesAntiviral AgentsHumansKiller Cells, NaturalMucous MembraneAntiviral AgentsAntivirus NK cell responseClinical virus isolatesHuman adenovirus infectionKiller cell immunoglobulin-like receptorsMucosal immunity in situMucosal NK cellsNK cell subsetsOcular surface

Identifiers

PMID35344177
OpenAlexW4225868866

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.