Evidence map›Paper›PMID 34572104›Full record

ArticleCells2021

Single Cell Analysis of Stored Red Blood Cells Using Ultra-High Throughput Holographic Cytometry.

Han-Sang Park, Hillel Price, Silvia Ceballos, Jen-Tsan Chi, Adam Wax

Open access · goldAbstract read
In one paragraph

Article in Cells, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
4.4field-weighted citation impact, top 5% 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

12 citing papers in PubMed, 31 citations in OpenAlex.

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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 at 1 institution in 1 country.

Han-Sang ParkDepartment of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Hillel PriceDepartment of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Silvia CeballosDepartment of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Jen-Tsan ChiDepartment of Molecular Genetics and Microbiology, Duke University, Durham, NC 27708, USA.ORCID 0000-0003-3433-903X
Adam WaxDepartment of Biomedical Engineering, Duke University, Durham, NC 27708, USA.ORCID 0000-0002-1827-5112
Duke University · US

Funding

NIH HHS 1R21-ES029791
6 · The paper itself

Abstract

Holographic cytometry is introduced as an ultra-high throughput implementation of quantitative phase imaging of single cells flowing through parallel microfluidic channels. Here, the approach was applied for characterizing the morphology of individual red blood cells during storage under regular blood bank conditions. Samples from five blood donors were examined, over 100,000 cells examined for each, at three time points. The approach allows high-throughput phase imaging of a large number of cells, greatly extending our ability to study cellular phenotypes using individual cell images. Holographic cytology images can provide measurements of multiple physical traits of the cells, including optical volume and area, which are observed to consistently change over the storage time. In addition, the large volume of cell imaging data can serve as training data for machine-learning algorithms. For the study here, logistic regression was used to classify the cells according to the storage time points. The analysis showed that at least 5000 cells are needed to ensure accuracy of the classifiers. Overall, results showed the potential of holographic cytometry as a diagnostic tool.

Indexed as

AlgorithmsMachine LearningErythrocytesFlow CytometryHolographyHumansImage Processing, Computer-AssistedSingle-Cell Analysishigh throughput cell screeningquantitative phase imagingred blood cell storage

Identifiers

PMID34572104
PMCPMC8465484
OpenAlexW3199156589

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.