Evidence map›Paper›PMID 42466013›Full record

ArticleAdvanced biosystems2018

Engineering Neural Tissue from Human Pluripotent Stem Cells Using Novel Small Molecule Releasing Microspheres.

Laura De la Vega, Karina Karmirian, Stephanie Michelle Willerth

Abstract read
In one paragraph

Article in Advanced biosystems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Tooth Formation: Are the Hardest Tissues of Human Body Hard to Regenerate?International journal of molecular sciences · 2020
    Review
  7. 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.

Laura De la VegaDepartment of Mechanical Engineering Division of Medical Sciences University of Victoria 3800 Finnerty Road Victoria BC V8P 5C2 Canada.
Karina KarmirianBiomedical Sciences Institute Federal University of Rio de Janeiro Av. Pedro Calmon, 550 Rio de Janeiro RJ 21941-901 Brazil.
Stephanie Michelle WillerthDepartment of Mechanical Engineering Division of Medical Sciences University of Victoria 3800 Finnerty Road Victoria BC V8P 5C2 Canada.ORCID https://orcid.org/0000-0002-1665-7723

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Here a novel technique for engineering neural tissue consisting of motor neurons by combining human-induced pluripotent stem cells (hiPSCs) with small molecules releasing microspheres is demonstrated. First, the small molecule purmorphamine (puro) is successfully encapsulated into poly ε-caprolactone (PCL) microspheres using a single emulsion oil-in-water (o/w) method for the first time with an efficiency of (84% ± 2.12%). These microspheres release 91% ± 1.7% of the encapsulated puro in a controlled fashion over 46 days. Puro microspheres, along with previously characterized retinoic acid (RA) releasing microspheres, are then incorporated into hiPSC aggregates to engineer neural tissue. The combination of puro and RA microspheres promotes hiPSC differentiation as indicated by the expression of multiple neural markers, including the neuronal marker β-tubulin III (βT-III), and the transcription factor Olig2 (7.69 ± 8.38%) on day 28. These tissues express the motor neuron marker HB9 (24.85 ± 4.51%) on day 35, and the mature motor neuron marker ChaT (12.35 ± 4.17%) on day 60. These engineered tissues can be used for regenerative medicine applications such as treating spinal cord injury (SCI), disease modeling, and drug screening.

Indexed as

controlled releasedrug deliverymotor neuronspurmorphamineretinoic acid

Identifiers

PMID42466013
PMCPMC13373853

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.