Evidence map›Paper›PMID 32094267›Full record

ArticleJournal of cell science2020

CTRL - a label-free artificial intelligence method for dynamic measurement of single-cell volume.

Kai Yao, Nash D Rochman, Sean X Sun

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cell science, 2020. 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. Article
  2. Rapid analysis of streaming platelet images by semi-unsupervised learning.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2021
    Article
  3. Review
  4. 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

3 authors.

Kai YaoDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Nash D RochmanInstitute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD 21218, USA.ORCID 0000-0002-7991-8505
Sean X SunDepartment of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA ssun@jhu.edu.ORCID 0000-0002-9077-7088

Funding

The Role of Physical Cues in Collective Cell InvasionU54CA210173 · NCI · JOHNS HOPKINS UNIVERSITY · PI PIENTA, KENNETH J., WIRTZ, DENIS · 2016 to 2020
$9.9M
T32: Predoctoral and Postdoctoral Training Program in Nanotechnology for Cancer ResearchT32CA153952 · NCI · JOHNS HOPKINS UNIVERSITY · PI Denis Wirtz · 2015 to 2026
$3.5M
The Role of Hydraulic Pressure in the Osmotic Engine Model of Cell MigrationR01GM134542 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI SEAN X SUN · 2019 to 2026
$2.1M
T-32 NIH Postdoctoral Training Program in Nanotechnology for Cancer Medicine NTCMT32CA130840 · NCI · JOHNS HOPKINS UNIVERSITY · PI WIRTZ, DENIS · 2008 to 2012
$1.2M
NCI NIH HHS T32 CA130840NCI NIH HHS T32 CA153952NCI NIH HHS U54 CA210173NIGMS NIH HHS R01 GM134542
6 · The paper itself

Abstract

Measuring the physical size of a cell is valuable in understanding cell growth control. Current single-cell volume measurement methods for mammalian cells are labor intensive, inflexible and can cause cell damage. We introduce CTRL: Cell Topography Reconstruction Learner, a label-free technique incorporating the deep learning algorithm and the fluorescence exclusion method for reconstructing cell topography and estimating mammalian cell volume from differential interference contrast (DIC) microscopy images alone. The method achieves quantitative accuracy, requires minimal sample preparation, and applies to a wide range of biological and experimental conditions. The method can be used to track single-cell volume dynamics over arbitrarily long time periods. For HT1080 fibrosarcoma cells, we observe that the cell size at division is positively correlated with the cell size at birth (sizer), and there is a noticeable reduction in cell size fluctuations at 25% completion of the cell cycle in HT1080 fibrosarcoma cells.

Indexed as

AlgorithmsArtificial IntelligenceAnimalsCell DivisionCell SizeAIArtificial intelligenceCell sizeCell volumeImage translationMachine learningML

Identifiers

PMID32094267
PMCPMC7174840

What OpenQuestion holds

Textmetadata
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.