Evidence map›Paper›PMID 28934883›Full record

ArticleCartilage2017

Contrast-Enhanced Computed Tomography Enables Quantitative Evaluation of Tissue Properties at Intrajoint Regions in Cadaveric Knee Cartilage.

Rachel C Stewart, Juuso T J Honkanen, Harri T Kokkonen, Virpi Tiitu, Simo Saarakkala, Antti Joukainen, Brian D Snyder, Jukka S Jurvelin, Mark W Grinstaff, Juha Töyräs

Abstract read
In one paragraph

Article in Cartilage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Synchrotron MicroCT Reveals the Potential of the Dual Contrast Technique for Quantitative Assessment of Human Articular Cartilage Composition.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2020
    Article
  10. Article
  11. Continuing Challenges in Advancing Preclinical Science in Skeletal Cell-Based Therapies and Tissue Regeneration.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2018
    Article
  12. 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

10 authors.

Rachel C Stewart1 Department of Biomedical Engineering, Boston University, Boston, MA, USA.
Juuso T J Honkanen3 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
Harri T Kokkonen4 Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
Virpi Tiitu5 Institute of Biomedicine, Anatomy, University of Eastern Finland, Kuopio, Finland.
Simo Saarakkala6 Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.
Antti Joukainen9 Department of Orthopaedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland.
Brian D Snyder2 Center for Advanced Orthopaedic Studies, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
Jukka S Jurvelin3 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
Mark W Grinstaff1 Department of Biomedical Engineering, Boston University, Boston, MA, USA.
Juha Töyräs3 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.

Funding

Characterizing electrostatic interactions between glycosaminoglycans and cationicR01GM098361 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI GRINSTAFF, MARK W., GUERMAZI, ALI · 2012 to 2015
$1.1M
European Research Council 336267NIGMS NIH HHS R01 GM098361
6 · The paper itself

Abstract

Objective The aim of this study was to investigate whether the concentration of the anionic contrast agent ioxaglate, as quantitated by contrast-enhanced computed tomography (CECT) using a clinical cone-beam CT (CBCT) instrument, reflects biochemical, histological, and biomechanical characteristics of articular cartilage imaged in an ex vivo, intact human knee joint. Design An osteoarthritic human cadaveric knee joint (91 years old) was injected with ioxaglate (36 mg I/mL) and imaged using CBCT over 61 hours of ioxaglate diffusion into cartilage. Following imaging, the joint surfaces were excised, rinsed to remove contrast agent, and compressive stiffness (equilibrium and instantaneous compressive moduli) was measured via indentation testing ( n = 17 sites). Each site was sectioned for histology and assessed for glycosaminoglycan content using digital densitometry of Safranin-O stained sections, Fourier transform infrared spectroscopy for collagen content, and morphology using both the Mankin and OARSI semiquantitative scoring systems. Water content was determined using mass change after lyophilization. Results CECT attenuation at all imaging time points, including those <1 hour of ioxaglate exposure, correlated significantly ( P < 0.05) with cartilage water and glycosaminoglycan contents, Mankin score, and both equilibrium and instantaneous compressive moduli. Early time points (<30 minutes) also correlated ( P < 0.05) with collagen content and OARSI score. Differences in cartilage quality between intrajoint regions were distinguishable at diffusion equilibrium and after brief ioxaglate exposure. Conclusions CECT with ioxaglate affords biochemical and biomechanical measurements of cartilage health and performance even after short, clinically relevant exposure times, and may be useful in the clinic as a means for detecting early signs of cartilage pathology.

Indexed as

cartilage imagingcone beam computed tomographycontrast enhanced computed tomographyosteoarthritis

Identifiers

PMID28934883
PMCPMC5613888

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.