Evidence map›Paper›PMID 38837346›Full record

ArticleMolecular biology of the cell2024

Image-based discrimination of the early stages of mesenchymal stem cell differentiation.

Justin Hoffman, Shiyuan Zheng, Huaiying Zhang, Robert F Murphy, Kris Noel Dahl

Abstract read
In one paragraph

Article in Molecular biology of the cell, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. 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.

Justin HoffmanDepartment of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213.
Shiyuan ZhengDepartment of Biomedical Engineering, College of Engineering, Carnegie Mellon University, Pittsburgh, PA 15213.
Huaiying ZhangDepartment of Biological Sciences, Mellon College of Science, Carnegie Mellon University, Pittsburgh, PA 15213.ORCID 0000-0002-1784-2664
Robert F MurphyDepartment of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213.ORCID 0000-0003-0358-901X
Kris Noel DahlDepartment of Biomedical Engineering, College of Engineering, Carnegie Mellon University, Pittsburgh, PA 15213.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mesenchymal stem cells (MSCs) are self-renewing, multipotent cells, which can be used in cellular and tissue therapeutics. MSCs cell number can be expanded in vitro, but premature differentiation results in reduced cell number and compromised therapeutic efficacies. Current techniques fail to discriminate the "stem-like" population from early stages (12 h) of differentiated MSC population. Here, we imaged nuclear structure and actin architecture using immunofluorescence and used deep learning-based computer vision technology to discriminate the early stages (6-12 h) of MSC differentiation. Convolutional neural network models trained by nucleus and actin images have high accuracy in reporting MSC differentiation; nuclear images alone can identify early stages of differentiation. Concurrently, we show that chromatin fluidity and heterochromatin levels or localization change during early MSC differentiation. This study quantifies changes in cell architecture during early MSC differentiation and describes a novel image-based diagnostic tool that could be widely used in MSC culture, expansion and utilization.

Indexed as

Cell DifferentiationMesenchymal Stem CellsActinsCell NucleusCells, CulturedChromatinDeep LearningHumansImage Processing, Computer-AssistedNeural Networks, ComputerActinsChromatin

Identifiers

PMID38837346
PMCPMC11321037

What OpenQuestion holds

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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.