Autofluorescent imprint of chronic constriction nerve injury identified by deep learning

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...

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Autores principales:	Martin E. Gosnell, Vasiliki Staikopoulos, Ayad G. Anwer, Saabah B. Mahbub, Mark R. Hutchinson, Sanam Mustafa, Ewa M. Goldys
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Chronic pain Autofluorescence imaging Spinal cord Allodynia Nerve injury Deep learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/d68ce565fc8f42179d2a360e47ef3e69
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id	oai:doaj.org-article:d68ce565fc8f42179d2a360e47ef3e69
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spelling	oai:doaj.org-article:d68ce565fc8f42179d2a360e47ef3e692021-11-12T04:26:05ZAutofluorescent imprint of chronic constriction nerve injury identified by deep learning1095-953X10.1016/j.nbd.2021.105528https://doaj.org/article/d68ce565fc8f42179d2a360e47ef3e692021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S0969996121002771https://doaj.org/toc/1095-953XOur 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.Martin E. GosnellVasiliki StaikopoulosAyad G. AnwerSaabah B. MahbubMark R. HutchinsonSanam MustafaEwa M. GoldysElsevierarticleChronic painAutofluorescence imagingSpinal cordAllodyniaNerve injuryDeep learningNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENNeurobiology of Disease, Vol 160, Iss , Pp 105528- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chronic pain Autofluorescence imaging Spinal cord Allodynia Nerve injury Deep learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Chronic pain Autofluorescence imaging Spinal cord Allodynia Nerve injury Deep learning Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Martin E. Gosnell Vasiliki Staikopoulos Ayad G. Anwer Saabah B. Mahbub Mark R. Hutchinson Sanam Mustafa Ewa M. Goldys Autofluorescent imprint of chronic constriction nerve injury identified by deep learning
description	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.
format	article
author	Martin E. Gosnell Vasiliki Staikopoulos Ayad G. Anwer Saabah B. Mahbub Mark R. Hutchinson Sanam Mustafa Ewa M. Goldys
author_facet	Martin E. Gosnell Vasiliki Staikopoulos Ayad G. Anwer Saabah B. Mahbub Mark R. Hutchinson Sanam Mustafa Ewa M. Goldys
author_sort	Martin E. Gosnell
title	Autofluorescent imprint of chronic constriction nerve injury identified by deep learning
title_short	Autofluorescent imprint of chronic constriction nerve injury identified by deep learning
title_full	Autofluorescent imprint of chronic constriction nerve injury identified by deep learning
title_fullStr	Autofluorescent imprint of chronic constriction nerve injury identified by deep learning
title_full_unstemmed	Autofluorescent imprint of chronic constriction nerve injury identified by deep learning
title_sort	autofluorescent imprint of chronic constriction nerve injury identified by deep learning
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/d68ce565fc8f42179d2a360e47ef3e69
work_keys_str_mv	AT martinegosnell autofluorescentimprintofchronicconstrictionnerveinjuryidentifiedbydeeplearning AT vasilikistaikopoulos autofluorescentimprintofchronicconstrictionnerveinjuryidentifiedbydeeplearning AT ayadganwer autofluorescentimprintofchronicconstrictionnerveinjuryidentifiedbydeeplearning AT saabahbmahbub autofluorescentimprintofchronicconstrictionnerveinjuryidentifiedbydeeplearning AT markrhutchinson autofluorescentimprintofchronicconstrictionnerveinjuryidentifiedbydeeplearning AT sanammustafa autofluorescentimprintofchronicconstrictionnerveinjuryidentifiedbydeeplearning AT ewamgoldys autofluorescentimprintofchronicconstrictionnerveinjuryidentifiedbydeeplearning
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Autofluorescent imprint of chronic constriction nerve injury identified by deep learning

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