Evidence map›Paper›PMID 25288519›Full record

ArticleActa biomaterialia2015

A hypothesis-driven parametric study of effects of polymeric scaffold properties on tissue engineered neovessel formation.

Kristin S Miller, Ramak Khosravi, Christopher K Breuer, Jay D Humphrey

Abstract read
In one paragraph

Article in Acta biomaterialia, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing 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

39 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Tissue Engineering of Vascular Grafts: A Case Report From Bench to Bedside and Back.Arteriosclerosis, thrombosis, and vascular biology · 2023
    Review
  9. Progress in the application of patch materials in cardiovascular surgery.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Computational models of cardiac hypertrophy.Progress in biophysics and molecular biology · 2021
    Review
  17. Review
  18. Article
  19. A computational bio-chemo-mechanical model of in vivo tissue-engineered vascular graft development.Integrative biology : quantitative biosciences from nano to macro · 2020
    Article
  20. 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

4 authors.

Kristin S MillerDepartment of Biomedical Engineering, Yale University, New Haven, CT, USA.
Ramak KhosraviDepartment of Biomedical Engineering, Yale University, New Haven, CT, USA.
Christopher K BreuerSurgical Research and Regenerative Medicine, Nationwide Children's Hospital, Columbus, OH, USA.
Jay D HumphreyDepartment of Biomedical Engineering, Yale University, New Haven, CT, USA; Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT, USA. Electronic address: jay.humphrey@yale.edu.

Funding

MEDICAL SCIENTIST TRAINING PROGRAMT32GM007205 · NIGMS · YALE UNIVERSITY · PI KAZMIERCZAK, BARBARA I · 1985 to 2019
$42.9M
Training In Multi-modality Molecular & Translational Cardiovascular ImagingT32HL098069 · NHLBI · YALE UNIVERSITY · PI JAMES S DUNCAN, Georges El Fakhri · 2010 to 2026
$5.2M
Mechanisms of Vascular Neotissue Formation In Tissue Engineered Vascular GraftsR01HL098228 · NHLBI · YALE UNIVERSITY · PI BREUER, CHRISTOPHER KANE · 2010 to 2018
$3.6M
NHLBI NIH HHS HL098069NHLBI NIH HHS HL098228NHLBI NIH HHS R01 HL098228NHLBI NIH HHS T32 HL098069NIGMS NIH HHS T32 GM007205
6 · The paper itself

Abstract

Continued advances in the tissue engineering of vascular grafts have enabled a paradigm shift from the desire to design for adequate suture retention, burst pressure and thrombo-resistance to the goal of achieving grafts having near native properties, including growth potential. Achieving this far more ambitious outcome will require the identification of optimal, not just adequate, scaffold structure and material properties. Given the myriad possible combinations of scaffold parameters, there is a need for a new strategy for reducing the experimental search space. Toward this end, we present a new modeling framework for in vivo neovessel development that allows one to begin to assess in silico the potential consequences of different combinations of scaffold structure and material properties. To restrict the number of parameters considered, we also utilize a non-dimensionalization to identify key properties of interest. Using illustrative constitutive relations for both the evolving fibrous scaffold and the neotissue that develops in response to inflammatory and mechanobiological cues, we show that this combined non-dimensionalization computational approach predicts salient aspects of neotissue development that depend directly on two key scaffold parameters, porosity and fiber diameter. We suggest, therefore, that hypothesis-driven computational models should continue to be pursued given their potential to identify preferred combinations of scaffold parameters that have the promise of improving neovessel outcome. In this way, we can begin to move beyond a purely empirical trial-and-error search for optimal combinations of parameters and instead focus our experimental resources on those combinations that are predicted to have the most promise.

Indexed as

Models, ChemicalTissue ScaffoldsAnimalsBlood VesselsCompressive StrengthComputer-Aided DesignComputer SimulationElastic ModulusEndothelial CellsEquipment DesignEquipment Failure AnalysisHardnessHumansMaterials TestingNeovascularization, PhysiologicPolymersPolymersConstrained mixture theoryFibrous scaffoldInterposition graftMouse modelPoly(glycolic acid)

Identifiers

PMID25288519
PMCPMC4256111

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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.