Data for assessing red blood cell deformability from microscopy images using deep learning.

Erik S Lamoureux, Emel Islamzada, Matthew V J Wiens, Kerryn Matthews, Simon P Duffy, Hongshen Ma

Open access · goldAbstract read

In one paragraph

Article in Data in brief, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

1.0field-weighted citation impact, top 23% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

Data for assessing red blood cell deformability from microscopy images using deep learning.Data in brief · 2023
Article

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

6 authors at 1 institution in 1 country.

Erik S LamoureuxDepartment of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada.

Emel IslamzadaCentre for Blood Research, University of British Columbia, 4th Floor, 2350 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada.

Matthew V J WiensSchool of Biomedical Engineering, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC V6T 1Z3, Canada.

Kerryn MatthewsDepartment of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada.

Simon P DuffyDepartment of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada.

Hongshen MaDepartment of Mechanical Engineering, University of British Columbia, 2054-6250 Applied Science Lane, Vancouver, BC V6T 1Z4, Canada.

University of British Columbia · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Red blood cell (RBC) deformability is a vital biophysical property that dictates the ability of these cells to repeatedly squeeze through small capillaries in the microvasculature. This capability is known to differ between individuals and degrades due to natural aging, pathology, and cold storage. There is great interest in measuring RBC deformability because this parameter is a potential biomarker of RBC quality for use in blood transfusions. Measuring this property from microscopy images would greatly reduce the effort required to acquire this information, as well as improve standardization across different centers. This dataset consists of live cell microscopy images of RBC samples from 10 healthy donors. Each RBC sample is sorted into fractions based on deformability using the microfluidic ratchet device. Each deformability fraction is imaged in microwell plates using a Nikon CFI S Plan Fluor ELWD 40 × objective and a Nikon DS-Qi2 CMOS camera on a Nikon Ti-2E inverted microscope. This data could be reused to develop deep learning algorithms to associate live cell images with cell deformability.

Indexed as

Computer visionDatasetDeep learningDeformabilityMachine learningMicrofluidicsOptical microscopyRed blood cells

Identifiers

PMID36798597

DOI10.1016/j.dib.2023.108928

PMCPMC9926190

OpenAlexW4318014841

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.