Evidence map›Paper›PMID 34318358›Full record

ArticleBiomechanics and modeling in mechanobiology2021

A multiscale optimisation method for bone growth scaffolds based on triply periodic minimal surfaces.

E F Lehder, I A Ashcroft, R D Wildman, L A Ruiz-Cantu, I Maskery

Abstract read
In one paragraph

Article in Biomechanics and modeling in mechanobiology, 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
–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

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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.

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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.

E F LehderCentre for Additive Manufacturing, Faculty of Engineering, The University of Nottingham, Nottingham, NG7 2RD, UK.
I A AshcroftCentre for Additive Manufacturing, Faculty of Engineering, The University of Nottingham, Nottingham, NG7 2RD, UK.
R D WildmanCentre for Additive Manufacturing, Faculty of Engineering, The University of Nottingham, Nottingham, NG7 2RD, UK.
L A Ruiz-CantuCentre for Additive Manufacturing, Faculty of Engineering, The University of Nottingham, Nottingham, NG7 2RD, UK.
I MaskeryCentre for Additive Manufacturing, Faculty of Engineering, The University of Nottingham, Nottingham, NG7 2RD, UK. ian.maskery@nottingham.ac.uk.ORCID http://orcid.org/0000-0003-1729-4837

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tissue engineered bone scaffolds are potential alternatives to bone allografts and autografts. Porous scaffolds based on triply periodic minimal surfaces (TPMS) are good candidates for tissue growth because they offer high surface-to-volume ratio, have tailorable stiffness, and can be easily fabricated by additive manufacturing. However, the range of TPMS scaffold types is extensive, and it is not yet clear which type provides the fastest cell or tissue growth while being sufficiently stiff to act as a bone graft. Nor is there currently an established methodology for TPMS bone scaffold design which can be quickly adopted by medical designers or biologists designing implants. In this study, we examine six TPMS scaffold types for use as tissue growth scaffolds and propose a general methodology to optimise their geometry. At the macro-scale, the optimisation routine ensures a scaffold stiffness within suitable limits for bone, while at the micro-scale it maximises the cell growth rate. The optimisation procedure also ensures the scaffold pores are of sufficient diameter to allow oxygen and nutrient delivery via capillaries. Of the examined TPMS structures, the Lidinoid and Split P cell types provide the greatest cell growth rates and are therefore the best candidates for bone scaffolds.

Indexed as

Biomechanical PhenomenaBone DevelopmentBone PlatesCell ProliferationFemoral FracturesFracture FixationHumansModels, BiologicalOsteoblastsPorositySurface PropertiesTissue EngineeringTissue ScaffoldsAdditive manufacturingBone scaffoldsLevel set methodMinimal surfacesMulti-scale modellingTissue regeneration

Identifiers

PMID34318358
PMCPMC8595174

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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.