Evidence map›Paper›PMID 38095813›Full record

ArticleBiomedical microdevices2023

Classification of fetal and adult red blood cells based on hydrodynamic deformation and deep video recognition.

Peter Johannes Tejlgaard Kampen, Gustav Ragnar Støttrup-Als, Nicklas Bruun-Andersen, Joachim Secher, Freja Høier, Anne Todsen Hansen, Morten Hanefeld Dziegiel, Anders Nymark Christensen, Kirstine Berg-Sørensen

Abstract read
In one paragraph

Article in Biomedical microdevices, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Peter Johannes Tejlgaard KampenDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
Gustav Ragnar Støttrup-AlsDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
Nicklas Bruun-AndersenDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
Joachim SecherDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
Freja HøierDepartment of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
Anne Todsen HansenDepartment of Clinical Immunology, University Hospital Copenhagen, Copenhagen, Denmark.
Morten Hanefeld DziegielDepartment of Clinical Immunology, University Hospital Copenhagen, Copenhagen, Denmark.
Anders Nymark ChristensenDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
Kirstine Berg-SørensenDepartment of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark. kibs@dtu.dk.ORCID http://orcid.org/0000-0002-9977-3980

Funding

Carlsbergfondet 2009-1-0286Strategiske Forskningsråd 10-092322/DSF
6 · The paper itself

Abstract

Flow based deformation cytometry has shown potential for cell classification. We demonstrate the principle with an injection moulded microfluidic chip from which we capture videos of adult and fetal red blood cells, as they are being deformed in a microfluidic chip. Using a deep neural network - SlowFast - that takes the temporal behavior into account, we are able to discriminate between the cells with high accuracy. The accuracy was larger for adult blood cells than for fetal blood cells. However, no significant difference was observed between donors of the two types.

Indexed as

HydrodynamicsMicrofluidic Analytical TechniquesErythrocytesFetusMicrofluidicsDeformationMicrofluidic flow cytometryNeural networkRed blood cellSlowFast

Identifiers

PMID38095813
PMCPMC10721708

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Registered trials

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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.