Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/137968
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Type: | Journal article |
Title: | Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signature |
Author: | Habibalahi, A. Campbell, J.M. Walters, S.N. Mahbub, S.B. Anwer, A.G. Grey, S.T. Goldys, E.M. |
Citation: | Computational and Structural Biotechnology Journal, 2023; 21:1851-1859 |
Publisher: | Elsevier BV |
Issue Date: | 2023 |
ISSN: | 2001-0370 2001-0370 |
Statement of Responsibility: | Abbas Habibalahi, Jared M. Campbell, Stacey N. Walters, Saabah B. Mahbub, Ayad G. Anwer, Shane T. Grey, Ewa M. Goldys |
Abstract: | Islets transplanted for type-1 diabetes have their viability reduced by warm ischemia, dimethyloxalylglycine (DMOG; hypoxia model), oxidative stress and cytokine injury. This results in frequent transplant failures and the major burden of patients having to undergo multiple rounds of treatment for insulin independence. Presently there is no reliable measure to assess islet preparation viability prior to clinical transplantation. We investigated deep morphological signatures (DMS) for detecting the exposure of islets to viability compromising insults from brightfield images. Accuracies ranged from 98 % to 68 % for; ROS damage, pro-inflammatory cytokines, warm ischemia and DMOG. When islets were disaggregated to single cells to enable higher throughput data collection, good accuracy was still obtained (83-71 %). Encapsulation of islets reduced accuracy for cytokine exposure, but it was still high (78 %). Unsupervised modelling of the DMS for islet preparations transplanted into a syngeneic mouse model was able to predict whether or not they would restore glucose control with 100 % accuracy. Our strategy for constructing DMS' is effective for the assessment of islet pre-transplant viability. If translated into the clinic, standard equipment could be used to prospectively identify non-functional islet preparations unable to contribute to the restoration of glucose control and reduce the burden of unsuccessful treatments. |
Keywords: | AI, artificial intelligence DMOG, dimethyloxalylglycine DMS, deep morphological signatures Deep morphological signature ECG, electrocardiogram EEG, electroencephalogram EMCCD, electron multiplying charge coupling device FD, Fisher Distance GSIS, glucose stimulated insulin secretion IoU, intersection over union MEG, magnetoencephalography MRI, magnetic resonance imaging PCA, principal component analysis Pancreatic islet ROS, reactive oxygen species SI, swarm intelligence SVM, support vector machine Transplantation |
Rights: | © 2023 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/li censes/by-nc-nd/4.0/). |
DOI: | 10.1016/j.csbj.2023.02.039 |
Grant ID: | http://purl.org/au-research/grants/arc/DP170101863 http://purl.org/au-research/grants/nhmrc/GNT1130222 http://purl.org/au-research/grants/nhmrc/GNT1146493 http://purl.org/au-research/grants/nhmrc/GNT1189235 http://purl.org/au-research/grants/nhmrc/GNT1140691 |
Published version: | http://dx.doi.org/10.1016/j.csbj.2023.02.039 |
Appears in Collections: | Public Health publications |
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hdl_137968.pdf | Published version | 5.6 MB | Adobe PDF | View/Open |
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