ArticleMacromolecular bioscience2019
Two-Photon Polymerization as a Tool for Studying 3D Printed Topography-Induced Stem Cell Fate.
Article in Macromolecular bioscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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.
Who cites it
9 citing papers in PubMed.
- Direct laser writing-enabled 3D printing strategies for microfluidic applications.Lab on a chip · 2024Review
- Highly Customizable 3D Microelectrode Arrays for In Vitro and In Vivo Neuronal Tissue Recordings.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Two-Photon Polymerized Shape Memory Microfibers: A New Mechanical Characterization Method in Liquid.Advanced functional materials · 2023Article
- Biomedical applications of three-dimensional bioprinted craniofacial tissue engineering.Bioengineering & translational medicine · 2023Review
- Applications of nanotechnology in 3D printed tissue engineering scaffolds.European journal of pharmaceutics and biopharmaceutics : official journal of Arbeitsgemeinschaft fur Pharmazeutische Verfahrenstechnik e.V · 2021Review
- Recent Advances in 3D Printing with Protein-Based Inks.Progress in polymer science · 2021Article
- A Tuneable, Photocurable, Poly(Caprolactone)-Based Resin for Tissue Engineering-Synthesis, Characterisation and Use in Stereolithography.Molecules (Basel, Switzerland) · 2021Article
- Great Expectations: Induced pluripotent stem cell technologies in neurodevelopmental impairments.International journal of medical sciences · 2021Review
- Development of High-Resolution Three-Dimensional-Printed Extracellular Matrix Scaffolds and Their Compatibility with Pluripotent Stem Cells and Early Retinal Cells.Journal of ocular pharmacology and therapeutics : the official journal of the Association for Ocular Pharmacology and TherapeuticsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Geometric topographies are known to influence cellular differentiation toward specific phenotypes, but to date the range of features and type of substrates that can be easily fabricated to study these interactions is somewhat limited. In this study, an emerging technology, two-photon polymerization, is used to print topological patterns with varying feature-size and thereby study their effect on cellular differentiation. This technique offers rapid manufacturing of topographical surfaces with good feature resolution for shapes smaller than 3 µm. Human-induced pluripotent stem cells, when attached to these substrates or a non-patterned control for 1 week, express an array of genetic markers that suggest their differentiation toward a heterogeneous population of multipotent progenitors from all three germ layers. Compared to the topographically smooth control, small features (1.6 µm) encourage differentiation toward ectoderm while large features (8 µm) inhibit self-renewal. This study demonstrates the potential of using two-photon polymerization to study and control stem cell fate as a function of substrate interactions. The ability to tailor and strategically design biomaterials in this way can enable more precise and efficient generation or maintenance of desired phenotypes in vitro and in vivo.
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Registered trials
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.