Evidence map›Paper›PMID 34750438›Full record

ArticleScientific reports2021

A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells.

Alexander Hillsley, Javier E Santos, Adrianne M Rosales

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
2.0field-weighted citation impact, top 12% 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

17 citing papers in PubMed, 27 citations in OpenAlex.

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  9. Dressed in Collagen: 2D and 3D Cardiac Fibrosis Models.International journal of molecular sciences · 2025
    Review
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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

3 authors at 1 institution in 1 country.

Alexander HillsleyMcKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA.
Javier E SantosHildebrand Department of Petroleum and Geosystems Engineering, University of Texas at Austin, Austin, TX, USA.
Adrianne M RosalesMcKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX, USA. arosales@che.utexas.edu.
The University of Texas at Austin · US

Funding

Burroughs Wellcome Fund 1015895Division of Materials Research 1720595
6 · The paper itself

Abstract

Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually leads to organ failure. On a cellular level, the development of fibrosis is associated with the activation of cardiac fibroblasts into myofibroblasts, a highly contractile and secretory phenotype. Myofibroblasts are commonly identified in vitro by the de novo assembly of alpha-smooth muscle actin stress fibers; however, there are few methods to automate stress fiber identification, which can lead to subjectivity and tedium in the process. To address this limitation, we present a computer vision model to classify and segment cells containing alpha-smooth muscle actin stress fibers into 2 classes (α-SMA SF

Indexed as

Deep LearningActinsArtificial IntelligenceCardiomyopathiesCell Culture TechniquesCells, CulturedElasticityFibroblastsHumansHydrogelsImage Processing, Computer-AssistedModels, CardiovascularMyocardiumMyofibroblastsStress FibersSurface PropertiesACTA2 protein, humanActinsHydrogels

Identifiers

PMID34750438
PMCPMC8575943
OpenAlexW3213271433

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.