Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/133010
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Type: | Journal article |
Title: | Autofluorescent imprint of chronic constriction nerve injury identified by deep learning |
Author: | Gosnell, M.E. Staikopoulos, V. Anwer, A.G. Mahbub, S.B. Hutchinson, M.R. Mustafa, S. Goldys, E.M. |
Citation: | Neurobiology of Disease, 2021; 160:105528-1-105528-11 |
Publisher: | Elsevier |
Issue Date: | 2021 |
ISSN: | 0969-9961 1095-953X |
Statement of Responsibility: | Martin E. Gosnell, Vasiliki Staikopoulos, Ayad G. Anwer, Saabah B. Mahbub, Mark R. Hutchinson, Sanam Mustafa, Ewa M. Goldys |
Abstract: | Our understanding of chronic pain and the underlying molecular mechanisms remains limited due to a lack of tools to identify the complex phenomena responsible for exaggerated pain behaviours. Furthermore, currently there is no objective measure of pain with current assessment relying on patient self-scoring. Here, we applied a fully biologically unsupervised technique of hyperspectral autofluorescence imaging to identify a complex signature associated with chronic constriction nerve injury known to cause allodynia. The analysis was carried out using deep learning/artificial intelligence methods. The central element was a deep learning autoencoder we developed to condense the hyperspectral channel images into a four- colour image, such that spinal cord tissue based on nerve injury status could be differentiated from control tissue. This study provides the first validation of hyperspectral imaging as a tool to differentiate tissues from nerve injured vs non-injured mice. The auto-fluorescent signals associated with nerve injury were not diffuse throughout the tissue but formed specific microscopic size regions. Furthermore, we identified a unique fluorescent signal that could differentiate spinal cord tissue isolated from nerve injured male and female animals. The identification of a specific global autofluorescence fingerprint associated with nerve injury and resultant neuropathic pain opens up the exciting opportunity to develop a diagnostic tool for identifying novel contributors to pain in individuals. |
Keywords: | Chronic pain; Autofluorescence imaging; Spinal cord; Allodynia; Nerve injury; Deep learning; Chronic constriction injury (CCI) |
Description: | Available online 7 October 2021 |
Rights: | © 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
DOI: | 10.1016/j.nbd.2021.105528 |
Grant ID: | http://purl.org/au-research/grants/arc/CE140100003 http://purl.org/au-research/grants/arc/FT180100565 |
Published version: | http://dx.doi.org/10.1016/j.nbd.2021.105528 |
Appears in Collections: | Physiology publications |
Files in This Item:
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hdl_133010.pdf | Published version | 6.15 MB | Adobe PDF | View/Open |
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