Evidence map›Paper›PMID 31872545›Full record

ArticleJournal of biophotonics2020

Acoustic radiation force optical coherence elastography for evaluating mechanical properties of soft condensed matters and its biological applications.

Hsiao-Chuan Liu, Piotr Kijanka, Matthew W Urban

Abstract read
In one paragraph

Article in Journal of biophotonics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Acoustic Force Elastography Microscopy.IEEE transactions on bio-medical engineering · 2023
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. 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.

Hsiao-Chuan LiuDepartment of Radiology, Mayo Clinic, Rochester, Minnesota.ORCID 0000-0002-5410-8821
Piotr KijankaDepartment of Radiology, Mayo Clinic, Rochester, Minnesota.
Matthew W UrbanDepartment of Radiology, Mayo Clinic, Rochester, Minnesota.ORCID 0000-0003-1360-4287

Funding

Measurement of Renal Viscoelastic Properties with UltrasoundR01DK092255 · NIDDK · MAYO CLINIC ROCHESTER · PI URBAN, MATTHEW WILLIAM · 2012 to 2022
$5.2M
NIDDK NIH HHS R01 DK092255
6 · The paper itself

Abstract

Evaluating mechanical properties of biological soft tissues and viscous mucus is challenging because of complicated dynamic behaviors. Soft condensed matter models have been successfully used to explain a number of dynamical behaviors. Here, we reported that optical coherence elastography (OCE) is capable of quantifying mechanical properties of soft condensed matters, micellar fluids. A 7.5 MHz focused transducer was utilized to generate acoustic radiation force exerted on the surface of soft condensed matters in order to produce Rayleigh waves. The waves were recorded by optical coherence tomography (OCT). The Kelvin-Voigt model was adopted to evaluate shear modulus and loss modulus of soft condensed matters. The results reported that various concentrations of micellar fluids can provide reasonable ranges of elasticity from 65.71 to 428.78 Pa and viscosity from 0.035 to 0.283 Pa·s, which are close to ranges for actual biological samples, like mucus. OCE might be a promising tool to differentiate pathologic mucus samples from healthy cases as advanced applications in the future.

Indexed as

Elasticity Imaging TechniquesAcousticsMechanical PhenomenaPhantoms, ImagingTomography, Optical Coherenceacoustic radiation forcemicellar fluidoptical coherence elastographyoptical coherence tomographysoft condensed matterultrasound

Identifiers

PMID31872545
PMCPMC7243171

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.