Evidence map›Paper›PMID 37074661›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Label-Free Identification of Persistent Particles in Association with Primary Immune Cells by Imaging Flow Cytometry.

Bradley Vis, Jonathan J Powell, Rachel E Hewitt

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. 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, top 88% 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, 0 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

3 authors at 1 institution in 1 country.

Bradley VisDepartment of Veterinary Medicine, University of Cambridge, Cambridge, UK.
Jonathan J PowellDepartment of Veterinary Medicine, University of Cambridge, Cambridge, UK.
Rachel E HewittDepartment of Veterinary Medicine, University of Cambridge, Cambridge, UK. reh63@cam.ac.uk.
University of Cambridge · GB

Funding

Medical Research Council MR/R005699/1
6 · The paper itself

Abstract

The frequency of human exposure to persistent particles via consumer products, air pollution, and work environments is a modern-day hazard and an active area of research. Particle density and crystallinity, which often dictate their persistence in biological systems, are associated with strong light absorption and reflectance. These attributes allow several persistent particle types to be identified without the use of additional labels using laser light-based techniques such as microscopy, flow cytometry, and imaging flow cytometry. This form of identification allows the direct analysis of environmental persistent particles in association with biological samples after in vivo studies and real-life exposures. Microscopy and imaging flow cytometry have progressed with computing capabilities and fully quantitative imaging techniques can now plausibly detail the interactions and effects of micron and nano-sized particles with primary cells and tissues. This chapter summarises studies which have utilized the strong light absorption and reflectance characteristics of particles for their detection in biological specimens. This is followed by the description of methods for the analysis of whole blood samples and the use of imaging flow cytometry to identify particles in association with primary peripheral blood phagocytic cells, using brightfield and darkfield parameters.

Indexed as

MicroscopyPhagocytesFlow CytometryHumansLightParticle SizeImaging flow cytometryLabel-free imagingMicroparticleNanoparticleNeutrophilsPersistent particlesWhole blood

Identifiers

PMID37074661
OpenAlexW4366350945

What OpenQuestion holds

Textmetadata
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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.