Evidence map›Paper›PMID 32776044›Full record

ReviewBiomaterials science2021

Medical imaging of tissue engineering and regenerative medicine constructs.

David B Berry, Erin K Englund, Shaochen Chen, Lawrence R Frank, Samuel R Ward

Open access · greenAbstract readReview
In one paragraph

Review in Biomaterials science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.9field-weighted citation impact, top 29% 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

6 citing papers in PubMed, 21 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
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

5 authors at 2 institutions in 1 country.

David B BerryDepartments of NanoEngineering, University of California, San Diego, USA. chen168@eng.ucsd.edu.
Erin K Englund
Shaochen Chen
Lawrence R Frank
Samuel R Ward
University of California System · USUniversity of California San Diego · US

Funding

New Quantitative Neuroimaging Metrics of Structural and Functional Connectivity of the Locus Coeruleus as a Novel Biomarker of Alzheimer's Disease Pathogenesis and ProgressionR01AG054049 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BONDI, MARK W, FRANK, LAWRENCE R · 2017 to 2021
$4.1M
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
NIAMS NIH HHS R01 AR070830NIA NIH HHS R01 AG054049
6 · The paper itself

Abstract

Advancement of tissue engineering and regenerative medicine (TERM) strategies to replicate tissue structure and function has led to the need for noninvasive assessment of key outcome measures of a construct's state, biocompatibility, and function. Histology based approaches are traditionally used in pre-clinical animal experiments, but are not always feasible or practical if a TERM construct is going to be tested for human use. In order to transition these therapies from benchtop to bedside, rigorously validated imaging techniques must be utilized that are sensitive to key outcome measures that fulfill the FDA standards for TERM construct evaluation. This review discusses key outcome measures for TERM constructs and various clinical- and research-based imaging techniques that can be used to assess them. Potential applications and limitations of these techniques are discussed, as well as resources for the processing, analysis, and interpretation of biomedical images.

Indexed as

Regenerative MedicineTissue EngineeringAnimalsDiagnostic ImagingHumans

Identifiers

PMID32776044
PMCPMC8262082
OpenAlexW3048093056

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.