Evidence map›Paper›PMID 30193908›Full record

ReviewBiomaterials2019

Microfluidic devices for disease modeling in muscle tissue.

Mollie M Smoak, Hannah A Pearce, Antonios G Mikos

Abstract readReview
In one paragraph

Review in Biomaterials, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

10 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Cell-Based Microfluidic Device Utilizing Cell Sheet Technology.Cyborg and bionic systems (Washington, D.C.) · 2022
    Article
  8. Article
  9. Article
  10. 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 at 1 institution in 1 country.

Mollie M SmoakDepartment of Bioengineering, Rice University, Houston, TX 77030, USA.
Hannah A PearceDepartment of Bioengineering, Rice University, Houston, TX 77030, USA.
Antonios G MikosDepartment of Bioengineering, Rice University, Houston, TX 77030, USA. Electronic address: mikos@rice.edu.
Rice University · US

Funding

Training and Dissemination CoreP41EB023833 · NIBIB · UNIV OF MARYLAND, COLLEGE PARK · PI FISHER, JOHN P, MIKOS, ANTONIOS G. · 2017 to 2021
$6.2M
Three-dimensional Model of Human Ewing SarcomaR01CA180279 · NCI · RICE UNIVERSITY · PI KASPER, FRED KURTIS, LUDWIG, JOSEPH A · 2014 to 2018
$1.8M
NCI NIH HHS R01 CA180279NIBIB NIH HHS P41 EB023833
6 · The paper itself

Abstract

Microfluidic devices have advanced significantly in recent years and are a promising technology for the field of tissue engineering. Highly sophisticated microfabrication techniques have paved the way for the development of complex ex vivo models capable of incorporating and measuring the real-time response of multiple cell types interacting together in a single system. Muscle-on-a-chip technology has drastically improved and serves as a drug screening platform for many muscular diseases such as muscular dystrophy, tendinosis, fibromyalgia, mitochondrial myopathy, and myasthenia gravis. This review seeks to communicate the gaps in knowledge of current muscular disease models and highlight the power of microfluidic devices in enabling researchers to better understand disease pathology and provide high throughput screening of therapeutics for muscular myopathies.

Indexed as

Lab-On-A-Chip DevicesAnimalsEquipment DesignHumansMicrofluidic Analytical TechniquesMicrotechnologyMusclesMuscular DiseasesTissue EngineeringEx vivo systemMicrofluidicsMuscleMuscle-on-a-chipMyopathy

Identifiers

PMID30193908
PMCPMC6395563
OpenAlexW2888891746

What OpenQuestion holds

Textmetadata
LicenceTDM
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.