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GAKTpore: Stereological Characterisation Methods for Porous Foams in Biomedical Applications.

ABSTRACT: In tissue engineering, scaffolds are a key component that possess a highly elaborate pore structure. Careful characterisation of such porous structures enables the prediction of a variety of large-scale biological responses. In this work, a rapid, efficient, and accurate methodology for 2D bulk porous structure analysis is proposed. The algorithm, "GAKTpore", creates a morphology map allowing quantification and visualisation of spatial feature variation. The software achieves 99.6% and 99.1% mean accuracy for pore diameter and shape factor identification, respectively. There are two main algorithm novelties within this work: (1) feature-dependant homogeneity map; (2) a new waviness function providing insights into the convexity/concavity of pores, important for understanding the influence on cell adhesion and proliferation. The algorithm is applied to foam structures, providing a full characterisation of a 10 mm diameter SEM micrograph (14,784 × 14,915 px) with 190,249 pores in ~9 min and has elucidated new insights into collagen scaffold formation by relating microstructural formation to the bulk formation environment. This novel porosity characterisation algorithm demonstrates its versatility, where accuracy, repeatability, and time are paramount. Thus, GAKTpore offers enormous potential to optimise and enhance scaffolds within tissue engineering.

SUBMITTER: Sheppard G

PROVIDER: S-EPMC7962185 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Motivation: Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art.Results: We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) prediction, protein-protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks.Availability and implementation: As part of our contributions in the paper, we develop an easy-to-use Python package with detailed instructions, BioNEV, available at: https://github.com/xiangyue9607/BioNEV, including all source code and datasets, to facilitate studying various graph embedding methods on biomedical tasks.Supplementary information: Supplementary data are available at Bioinformatics online.

Project description:The accuracy of quantitative stereological analysis tools such as the (physical) disector method substantially depends on the precise determination of the thickness of the analyzed histological sections. One conventional method for measurement of histological section thickness is to re-embed the section of interest vertically to its original section plane. The section thickness is then measured in a subsequently prepared histological section of this orthogonally re-embedded sample. However, the orthogonal re-embedding (ORE) technique is quite work- and time-intensive and may produce inaccurate section thickness measurement values due to unintentional slightly oblique (non-orthogonal) positioning of the re-embedded sample-section. Here, an improved ORE method is presented, allowing for determination of the factual section plane angle of the re-embedded section, and correction of measured section thickness values for oblique (non-orthogonal) sectioning. For this, the analyzed section is mounted flat on a foil of known thickness (calibration foil) and both the section and the calibration foil are then vertically (re-)embedded. The section angle of the re-embedded section is then calculated from the deviation of the measured section thickness of the calibration foil and its factual thickness, using basic geometry. To find a practicable, fast, and accurate alternative to ORE, the suitability of spectral reflectance (SR) measurement for determination of plastic section thicknesses was evaluated. Using a commercially available optical reflectometer (F20, Filmetrics®, USA), the thicknesses of 0.5 ?m thick semi-thin Epon (glycid ether)-sections and of 1-3 ?m thick plastic sections (glycolmethacrylate/ methylmethacrylate, GMA/MMA), as regularly used in physical disector analyses, could precisely be measured within few seconds. Compared to the measured section thicknesses determined by ORE, SR measures displayed less than 1% deviation. Our results prove the applicability of SR to efficiently provide accurate section thickness measurements as a prerequisite for reliable estimates of dependent quantitative stereological parameters.

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GAKTpore: Stereological Characterisation Methods for Porous Foams in Biomedical Applications.

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