Artificial neural networks coupled with MALDI-TOF MS serum fingerprinting to classify and diagnose pathological pain subtypes in preclinical models
dc.contributor.author
dc.date.accessioned
2023-01-10T14:44:09Z
dc.date.available
2023-01-10T14:44:09Z
dc.date.issued
2022-12-30
dc.identifier.issn
1948-7193
dc.identifier.uri
dc.description.abstract
Pathological pain subtypes can be classified as either neuropathic pain, caused by a somatosensory nervous system lesion or disease, or nociplastic pain, which develops without evidence of somatosensory system damage. Since there is no gold standard for the diagnosis of pathological pain subtypes, the proper classification of individual patients is currently an unmet challenge for clinicians. While the determination of specific biomarkers for each condition by current biochemical techniques is a complex task, the use of multimolecular techniques, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), combined with artificial intelligence allows specific fingerprints for pathological pain-subtypes to be obtained, which may be useful for diagnosis. We analyzed whether the information provided by the mass spectra of serum samples of four experimental models of neuropathic and nociplastic pain combined with their functional pain outcomes could enable pathological pain subtype classification by artificial neural networks. As a result, a simple and innovative clinical decision support method has been developed that combines MALDI-TOF MS serum spectra and pain evaluation with its subsequent data analysis by artificial neural networks and allows the identification and classification of pathological pain subtypes in experimental models with a high level of specificity
dc.description.sponsorship
This work was supported by the University of Girona
(MPCUdG2016/087) and La MARATÓ de TV3 Foundation
(201705.30.31) from Catalonia; and by Masaryk University
(MUNI/11/ACC/3/2022, MUNI/A/1398/2021, MUNI/A/
1412/2021), Brno, Czech Republic. The authors also thank the
staff of the animal care facility of the University of Barcelona
(Campus Bellvitge) for their skillful technical assistance. The
Mass Spectrometry Core Facility of FNUSA-ICRC is acknowledged for their support and assistance in this work
Open Access funding provided thanks to the CRUE-CSIC agreement with ACS
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
American Chemical Society (ACS)
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1021/acschemneuro.2c00665
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ACS Chemical Neuroscience, 2022, vol. undef, núm. undef, p. undef
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Articles publicats (D-CM)
dc.rights
Reconeixement 4.0 Internacional
dc.rights.uri
dc.source
Deulofeu, Meritxell Peña Mendez, Eladia Vaňhara, Petr Havel, Josef Moráň, Luká Pečinka, Luká Bagó Mas, Anna Verdú Navarro, Enrique Salvadó Martín, Victòria Boadas i Vaello, Pere 2022 Artificial neural networks coupled with MALDI-TOF MS serum fingerprinting to classify and diagnose pathological pain subtypes in preclinical models ACS Chemical Neuroscience undef undef undef
dc.subject
dc.title
Artificial neural networks coupled with MALDI-TOF MS serum fingerprinting to classify and diagnose pathological pain subtypes in preclinical models
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
035915
dc.type.peerreviewed
peer-reviewed