Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F23%3A00079706" target="_blank" >RIV/00159816:_____/23:00079706 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14110/23:00130292 RIV/00209805:_____/23:00079121
Result on the web
<a href="https://pubs.acs.org/doi/10.1021/acschemneuro.2c00665" target="_blank" >https://pubs.acs.org/doi/10.1021/acschemneuro.2c00665</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1021/acschemneuro.2c00665" target="_blank" >10.1021/acschemneuro.2c00665</a>
Alternative languages
Result language
angličtina
Original language name
Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models
Original language description
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-offlight 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ACS Chemical Neuroscience
ISSN
1948-7193
e-ISSN
1948-7193
Volume of the periodical
14
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
300-311
UT code for WoS article
000907867400001
EID of the result in the Scopus database
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