Evidence map›Paper›PMID 30183050›Full record

ReviewLab on a chip2018

Development and application of human skeletal muscle microphysiological systems.

George A Truskey

Abstract readReview
In one paragraph

Review in Lab on a chip, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Microphysiological systems: What it takes for community adoption.Experimental biology and medicine (Maywood, N.J.) · 2021
    Review
  11. Review
  12. 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

1 author.

George A TruskeyDepartment of Biomedical Engineering, Duke University, 1427 CIEMAS, 101 Science Drive, Durham, NC 27708-0281, USA. gtruskey@duke.edu.

Funding

Systemic Inflammation in Microphysiological Models of Muscle and Vascular DiseaseUG3TR002142 · NCATS · DUKE UNIVERSITY · PI TRUSKEY, GEORGE A · 2017 to 2018
$2.1M
Developing An In Vitro Human Myobundle Model Of Rheumatoid ArthritisR21AR072283 · NIAMS · DUKE UNIVERSITY · PI TRUSKEY, GEORGE A · 2017 to 2018
$373k
NCATS NIH HHS UG3 TR002142NIAMS NIH HHS R21 AR072283
6 · The paper itself

Abstract

A number of major disease states involve skeletal muscle, including type 2 diabetes, muscular dystrophy, sarcopenia and cachexia arising from cancer or heart disease. Animals do not accurately represent many of these disease states. Human skeletal muscle microphysiological systems derived from primary or induced pluripotent stem cells (hPSCs) can provide an in vitro model of genetic and chronic diseases and assess individual variations. Three-dimensional culture systems more accurately represent skeletal muscle function than do two-dimensional cultures. While muscle biopsies enable culture of primary muscle cells, hPSCs provide the opportunity to sample a wider population of donors. Recent advances to promote maturation of PSC-derived skeletal muscle provide an alternative to primary cells. While contractile function is often measured in three-dimensional cultures and several systems exist to characterize contraction of small numbers of muscle fibers, there is a need for functional measures of metabolism suited for microphysiological systems. Future research should address generation of well-differentiated hPSC-derived muscle cells, enabling muscle repair in vitro, and improved disease models.

Indexed as

AnimalsCell DifferentiationHumansInduced Pluripotent Stem CellsMuscle ContractionMuscle, SkeletalTissue Engineering

Identifiers

PMID30183050
PMCPMC6177290

What OpenQuestion holds

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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.