Autofluorescent imprint of chronic constriction nerve injury identified by deep learning

Simple item page
dc.contributor.authorGosnell, M.E.
dc.contributor.authorStaikopoulos, V.
dc.contributor.authorAnwer, A.G.
dc.contributor.authorMahbub, S.B.
dc.contributor.authorHutchinson, M.R.
dc.contributor.authorMustafa, S.
dc.contributor.authorGoldys, E.M.
dc.date.issued2021
dc.descriptionAvailable online 7 October 2021
dc.description.abstractOur 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.
dc.description.statementofresponsibilityMartin E. Gosnell, Vasiliki Staikopoulos, Ayad G. Anwer, Saabah B. Mahbub, Mark R. Hutchinson, Sanam Mustafa, Ewa M. Goldys
dc.identifier.citationNeurobiology of Disease, 2021; 160:105528-1-105528-11
dc.identifier.doi10.1016/j.nbd.2021.105528
dc.identifier.issn0969-9961
dc.identifier.issn1095-953X
dc.identifier.orcidHutchinson, M.R. [0000-0003-2154-5950]
dc.identifier.orcidMustafa, S. [0000-0002-8677-5151]
dc.identifier.urihttps://hdl.handle.net/2440/133010
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/arc/CE140100003
dc.relation.granthttp://purl.org/au-research/grants/arc/FT180100565
dc.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/).
dc.source.urihttps://doi.org/10.1016/j.nbd.2021.105528
dc.subjectChronic pain; Autofluorescence imaging; Spinal cord; Allodynia; Nerve injury; Deep learning; Chronic constriction injury (CCI)
dc.titleAutofluorescent imprint of chronic constriction nerve injury identified by deep learning
dc.typeJournal article
pubs.publication-statusPublished

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
hdl_133010.pdf
Size:
6.01 MB
Format:
Adobe Portable Document Format
Description:
Published version
Download

Collections

Physiology publications