Evidence map›Paper›PMID 34811890›Full record

ReviewCytometry. Part A : the journal of the International Society for Analytical Cytology2023

COVID-19: Using high-throughput flow cytometry to dissect clinical heterogeneity.

Irene Del Molino Del Barrio, Thomas S Hayday, Adam G Laing, Adrian C Hayday, Francesca Di Rosa

Abstract readReview
In one paragraph

Review in Cytometry. Part A : the journal of the International Society for Analytical Cytology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. COVID-19: Using high-throughput flow cytometry to dissect clinical heterogeneity.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2023
    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

5 authors.

Irene Del Molino Del BarrioPeter Gorer Department of Immunobiology, King's College London, London, UK.
Thomas S HaydayPeter Gorer Department of Immunobiology, King's College London, London, UK.
Adam G LaingPeter Gorer Department of Immunobiology, King's College London, London, UK.
Adrian C HaydayPeter Gorer Department of Immunobiology, King's College London, London, UK.
Francesca Di RosaInstitute of Molecular Biology and Pathology, National Research Council of Italy, Rome, Italy.ORCID 0000-0003-0252-9138

Funding

Cancer Research UK FC001003Medical Research Council MR/V040162/1Wellcome Trust FC001003
6 · The paper itself

Abstract

Here we consider how high-content flow cytometric methodology at appropriate scale and throughput rapidly provided meaningful biological data in our recent studies of COVID-19, which we discuss in the context of other similar investigations. In our work, high-throughput flow cytometry was instrumental to identify a consensus immune signature in COVID-19 patients, and to investigate the impact of SARS-CoV-2 exposure on patients with either solid or hematological cancers. We provide here some examples of our 'holistic' approach, in which flow cytometry data generated by lymphocyte and myelomonocyte panels were integrated with other analytical metrics, including SARS-CoV-2-specific serum antibody titers, plasma cytokine/chemokine levels, and in-depth clinical annotation. We report how selective differences between T cell subsets were revealed by a newly described flow cytometric T

Indexed as

COVID-19CytokinesFlow CytometryHumansSARS-CoV-2T-Lymphocyte SubsetsCytokinescancercell cycleCOVID-19DNA dyeHigh-throughput flow cytometryKi-67

Identifiers

PMID34811890
PMCPMC9011838

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.