Evidence map›Paper›PMID 24753483›Full record

ArticleInternational journal for numerical methods in biomedical engineering2014

Modeling and simulation of the effects of cyclic loading on articular cartilage lesion formation.

Xiayi Wang, Bruce P Ayati, Marc J Brouillete, Jason M Graham, Prem S Ramakrishnan, James A Martin

Abstract read
In one paragraph

Article in International journal for numerical methods in biomedical engineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Complementary models reveal cellular responses to contact stresses that contribute to post-traumatic osteoarthritis.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2017
    Article
  6. Mathematics as a conduit for translational research in post-traumatic osteoarthritis.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2017
    Article
  7. Article
  8. Article
  9. Article
  10. 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

6 authors.

Xiayi WangProgram in Applied Mathematical and Computational Sciences, University of Iowa, Iowa City, IA 52242.
Bruce P Ayati
Marc J Brouillete
Jason M Graham
Prem S Ramakrishnan
James A Martin

Funding

Tissue and Experimental Modeling CoreP50AR055533 · NIAMS · UNIVERSITY OF IOWA · PI GOETZ, JESSICA E · 2007 to 2016
$14.9M
NIAMS NIH HHS 1 P50 AR055533NIAMS NIH HHS P50 AR055533
6 · The paper itself

Abstract

We present a model of articular cartilage lesion formation to simulate the effects of cyclic loading. This model extends and modifies the reaction-diffusion-delay model by Graham et al., 2012 for the spread of a lesion formed though a single traumatic event. Our model represents 'implicitly' the effects of loading, meaning through a cyclic sink term in the equations for live cells. Our model forms the basis for in silico studies of cartilage damage relevant to questions in osteoarthritis, for example, that may not be easily answered through in vivo or in vitro studies. Computational results are presented that indicate the impact of differing levels of erythropoietin on articular cartilage lesion abatement.

Indexed as

Models, BiologicalCartilage, ArticularChondrocytesComputer SimulationDiffusionEpoetin AlfaErythropoietinHumansModels, TheoreticalOsteoarthritisRecombinant ProteinsStress, MechanicalEpoetin AlfaErythropoietinRecombinant Proteinsarticular cartilagedelayreaction-diffusion

Identifiers

PMID24753483
PMCPMC4950512

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.