Evidence map›Paper›PMID 28338417›Full record

ArticleTissue engineering. Part A2017

David B Berry, Shangting You, John Warner, Lawrence R Frank, Shaochen Chen, Samuel R Ward

Open access · greenAbstract read
In one paragraph

Article in Tissue engineering. Part A, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
4.1field-weighted citation impact, top 6% 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

18 citing papers in PubMed, 40 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

David B Berry1 Department of Bioengineering, University of California San Diego , La Jolla, California.
Shangting You2 Department of Nanoengineering, University of California San Diego , La Jolla, California.
John Warner2 Department of Nanoengineering, University of California San Diego , La Jolla, California.
Lawrence R Frank3 Department of Radiology, University of California San Diego , La Jolla, California.
Shaochen Chen2 Department of Nanoengineering, University of California San Diego , La Jolla, California.
Samuel R Ward1 Department of Bioengineering, University of California San Diego , La Jolla, California.
University of California, San Diego · USLa Jolla Bioengineering Institute · US

Funding

Non Invasive Measurements of Muscle Microstructures Assessed by Diffusion Tensor ImagingR01AR070830 · NIAMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI David Barnes Berry, LAWRENCE R FRANK · 2017 to 2026
$4.0M
Rapid 3D bioprinting of biomimetic vascularized tissue constructsR01EB021857 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CHEN, SHAOCHEN, DOKMECI, MEHMET REMZI · 2016 to 2019
$2.1M
Diffusion Imaging in Gray MatterR01MH096100 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FRANK, LAWRENCE R · 2012 to 2016
$1.9M
NIAMS NIH HHS R01 AR070830NIBIB NIH HHS R01 EB021857NIMH NIH HHS R01 MH096100
6 · The paper itself

Abstract

The ability to noninvasively assess skeletal muscle microstructure, which predicts function and disease, would be of significant clinical value. One method that holds this promise is diffusion tensor magnetic resonance imaging (DT-MRI), which is sensitive to the microscopic diffusion of water within tissues and has become ubiquitous in neuroimaging as a way of assessing neuronal structure and damage. However, its application to the assessment of changes in muscle microstructure associated with injury, pathology, or age remains poorly defined, because it is difficult to precisely control muscle microstructural features in vivo. However, recent advances in additive manufacturing technologies allow precision-engineered diffusion phantoms with histology informed skeletal muscle geometry to be manufactured. Therefore, the goal of this study was to develop skeletal muscle phantoms at relevant size scales to relate microstructural features to MRI-based diffusion measurements. A digital light projection based rapid 3D printing method was used to fabricate polyethylene glycol diacrylate based diffusion phantoms with (1) idealized muscle geometry (no geometry; fiber sizes of 30, 50, or 70 μm or fiber size of 50 μm with 40% of walls randomly deleted) or (2) histology-based geometry (normal and after 30-days of denervation) containing 20% or 50% phosphate-buffered saline (PBS). Mean absolute percent error (8%) of the printed phantoms indicated high conformity to templates when "fibers" were >50 μm. A multiple spin-echo echo planar imaging diffusion sequence, capable of acquiring diffusion weighted data at several echo times, was used in an attempt to combine relaxometry and diffusion techniques with the goal of separating intracellular and extracellular diffusion signals. When fiber size increased (30-70 μm) in the 20% PBS phantom, fractional anisotropy (FA) decreased (0.32-0.26) and mean diffusivity (MD) increased (0.44 × 10

Indexed as

Muscle, SkeletalPhantoms, ImagingPrinting, Three-DimensionalAnimalsDiffusion Tensor ImagingHumansTissue Scaffolds3D printingdiffusion tensor imaginghydrogel scaffoldMRImuscle

Identifiers

PMID28338417
PMCPMC5610393
OpenAlexW2765598014

What OpenQuestion holds

Textmetadata
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.