Evidence map›Paper›PMID 31622051›Full record

ReviewAdvanced healthcare materials2020

Vascularized and Innervated Skeletal Muscle Tissue Engineering.

Jordana Gilbert-Honick, Warren Grayson

Abstract readReview
In one paragraph

Review in Advanced healthcare materials, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.

0numbers the graph read from it
0cells of the map it votes in
96citing 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

96 citing papers in PubMed.

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  18. The machinery of healthy vasoconstriction: an overview.Pflugers Archiv : European journal of physiology · 2025
    Review
  19. Article
  20. Article

36 more citing papers are in PubMed but not listed here.

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

2 authors.

Jordana Gilbert-HonickTranslational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21231, USA.
Warren GraysonTranslational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD, 21231, USA.ORCID 0000-0001-6099-6469

Funding

Regenerating Vascularized and Innervated Skeletal Muscle to Treat VML DefectsR01AR077581 · NIAMS · JOHNS HOPKINS UNIVERSITY · PI GRAYSON, WARREN L, LEE, GABSANG · 2020 to 2024
$2.2M
Engineering Vascularized Skeletal Muscle Tissues with Adipose-derived Stem CellsF31AR071759 · NIAMS · JOHNS HOPKINS UNIVERSITY · PI GILBERT-HONICK, JORDANA · 2018 to 2019
$89k
Maryland Stem Cell Research Fund 2016-MSCRFI-2692NIAMS NIH HHS F31 AR071759NIAMS NIH HHS R01 AR077581
6 · The paper itself

Abstract

Volumetric muscle loss (VML) is a devastating loss of muscle tissue that overwhelms the native regenerative properties of skeletal muscle and results in lifelong functional deficits. There are currently no treatments for VML that fully recover the lost muscle tissue and function. Tissue engineering presents a promising solution for VML treatment and significant research has been performed using tissue engineered muscle constructs in preclinical models of VML with a broad range of defect locations and sizes, tissue engineered construct characteristics, and outcome measures. Due to the complex vascular and neural anatomy within skeletal muscle, regeneration of functional vasculature and nerves is vital for muscle recovery following VML injuries. This review aims to summarize the current state of the field of skeletal muscle tissue engineering using 3D constructs for VML treatment with a focus on studies that have promoted vascular and neural regeneration within the muscle tissue post-VML.

Indexed as

Tissue EngineeringAnimalsBlood VesselsHumansHydrogelsMuscle, SkeletalMuscular DiseasesRegenerationStem CellsTissue ScaffoldsHydrogelsneuromuscular junctionsskeletal muscle regenerationtissue engineeringvascularizationvolumetric muscle loss

Identifiers

PMID31622051
PMCPMC6986325

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.