Artificial Neural Networks Coupled with MALDI-TOF MS Serum
Fingerprinting To Classify and Diagnose Pathological Pain
Subtypes in Preclinical Models

J 2023

Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models

DEULOFEU FIGUERAS, Meritxell, Eladia M. PEÑA-MÉNDEZ, Petr VAŇHARA, Josef HAVEL, Lukáš MORÁŇ et. al.

Basic information

Original name

Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models

Authors

DEULOFEU FIGUERAS, Meritxell (724 Spain, belonging to the institution), Eladia M. PEÑA-MÉNDEZ, Petr VAŇHARA (203 Czech Republic, belonging to the institution), Josef HAVEL (203 Czech Republic, belonging to the institution), Lukáš MORÁŇ (203 Czech Republic, belonging to the institution), Lukáš PEČINKA (203 Czech Republic, belonging to the institution), Anna BAGÓ-MAS, Enrique VERDÚ, Victoria SALVADÓ and Pere BOADAS-VAELLO (guarantor)

Edition

ACS Chemical Neuroscience, American Chemical Society, 2023, 1948-7193

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30400 3.4 Medical biotechnology

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 5.000 in 2022

RIV identification code

RIV/00216224:14110/23:00130292

Organization unit

Faculty of Medicine

DOI

http://dx.doi.org/10.1021/acschemneuro.2c00665

UT WoS

000907867400001

Keywords in English

neuropathic pain; fibromyalgia; mass spectrometry; artificial intelligence; MALDI-TOF MS; diagnostics

Abstract

V originále

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.

Links

MUNI/A/1298/2022, interní kód MU

Name: Základní a aplikovaný výzkum a vývoj metod chemické a fyzikálně chemické analýzy pro studium přírody a pokročilé technologie

Investor: Masaryk University, Basic and applied research and development of chemical and physicochemical analytical methods for the study of nature and advanced technology

MUNI/A/1398/2021, interní kód MU

Name: Zdroje pro tkáňové inženýrství 12 (Acronym: TissueEng 12)

Investor: Masaryk University

MUNI/A/1412/2021, interní kód MU

Name: Výzkum geologických, biologických a pokročilých syntetických materiálů metodami analytickými a fyzikálně-chemickými (Acronym: ANFYZCHEM)

Investor: Masaryk University, Research of geological, biological and advanced synthetic materials by analytical and physico-chemical methods

MUNI/11/ACC/3/2022, interní kód MU

Name: Bioanalytical quality control of cGMP/ATMP-grade stem cells and progenitors

Investor: Masaryk University, Accelerate

Citovat

DEULOFEU FIGUERAS, Meritxell, Eladia M. PEÑA-MÉNDEZ, Petr VAŇHARA, Josef HAVEL, Lukáš MORÁŇ, Lukáš PEČINKA, Anna BAGÓ-MAS, Enrique VERDÚ, Victoria SALVADÓ and Pere BOADAS-VAELLO. Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models. ACS Chemical Neuroscience. American Chemical Society, 2023, vol. 14, No 2, p. 300-311. ISSN 1948-7193. Available from: https://dx.doi.org/10.1021/acschemneuro.2c00665.

@article{2253372,
   author = {Deulofeu Figueras, Meritxell and PeñaandMéndez, Eladia M. and Vaňhara, Petr and Havel, Josef and Moráň, Lukáš and Pečinka, Lukáš and BagóandMas, Anna and Verdú, Enrique and Salvadó, Victoria and BoadasandVaello, Pere},
   article_number = {2},
   doi = {http://dx.doi.org/10.1021/acschemneuro.2c00665},
   keywords = {neuropathic pain; fibromyalgia; mass spectrometry; artificial intelligence; MALDI-TOF MS; diagnostics},
   language = {eng},
   issn = {1948-7193},
   journal = {ACS Chemical Neuroscience},
   title = {Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models},
   url = {https://pubs.acs.org/doi/full/10.1021/acschemneuro.2c00665?cookieSet=1},
   volume = {14},
   year = {2023}
}

TY  - JOUR
ID  - 2253372
AU  - Deulofeu Figueras, Meritxell - Peña-Méndez, Eladia M. - Vaňhara, Petr - Havel, Josef - Moráň, Lukáš - Pečinka, Lukáš - Bagó-Mas, Anna - Verdú, Enrique - Salvadó, Victoria - Boadas-Vaello, Pere
PY  - 2023
TI  - Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models
JF  - ACS Chemical Neuroscience
VL  - 14
IS  - 2
SP  - 300-311
EP  - 300-311
PB  - American Chemical Society
SN  - 19487193
KW  - neuropathic pain
KW  - fibromyalgia
KW  - mass spectrometry
KW  - artificial intelligence
KW  - MALDI-TOF MS
KW  - diagnostics
UR  - https://pubs.acs.org/doi/full/10.1021/acschemneuro.2c00665?cookieSet=1
N2  - 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.
ER  -

DEULOFEU FIGUERAS, Meritxell, Eladia M. PEÑA-MÉNDEZ, Petr VAŇHARA, Josef HAVEL, Lukáš MORÁŇ, Lukáš PEČINKA, Anna BAGÓ-MAS, Enrique VERDÚ, Victoria SALVADÓ and Pere BOADAS-VAELLO. Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models. \textit{ACS Chemical Neuroscience}. American Chemical Society, 2023, vol.~14, No~2, p.~300-311. ISSN~1948-7193. Available from: https://dx.doi.org/10.1021/acschemneuro.2c00665.

Detailed Information on Publication Record