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.

Základní údaje

Originální název

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

Autoři

DEULOFEU FIGUERAS, Meritxell (724 Španělsko, domácí), Eladia M. PEÑA-MÉNDEZ, Petr VAŇHARA (203 Česká republika, domácí), Josef HAVEL (203 Česká republika, domácí), Lukáš MORÁŇ (203 Česká republika, domácí), Lukáš PEČINKA (203 Česká republika, domácí), Anna BAGÓ-MAS, Enrique VERDÚ, Victoria SALVADÓ a Pere BOADAS-VAELLO (garant)

Vydání

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

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30400 3.4 Medical biotechnology

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 5.000 v roce 2022

Kód RIV

RIV/00216224:14110/23:00130292

Organizační jednotka

Lékařská fakulta

DOI

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

UT WoS

000907867400001

Klíčová slova anglicky

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

Štítky

14110517, podil, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 21. 3. 2024 11:30, Mgr. Marie Šípková, DiS.

Anotace

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.

Návaznosti

MUNI/A/1298/2022, interní kód MU

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

Investor: Masarykova univerzita, Základní a aplikovaný výzkum a vývoj metod chemické a fyzikálně chemické analýzy pro studium přírody a pokročilé technologie

MUNI/A/1398/2021, interní kód MU

Název: Zdroje pro tkáňové inženýrství 12 (Akronym: TissueEng 12)

Investor: Masarykova univerzita, Zdroje pro tkáňové inženýrství 12

MUNI/A/1412/2021, interní kód MU

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

Investor: Masarykova univerzita, Výzkum geologických, biologických a pokročilých syntetických materiálů metodami analytickými a fyzikálně-chemickými

MUNI/11/ACC/3/2022, interní kód MU

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

Investor: Masarykova univerzita, Bioanalytical quality control of cGMP/ATMP-grade stem cells and progenitors, Accelerate

Citovat

DEULOFEU FIGUERAS, Meritxell, Eladia M. PEÑA-MÉNDEZ, Petr VAŇHARA, Josef HAVEL, Lukáš MORÁŇ, Lukáš PEČINKA, Anna BAGÓ-MAS, Enrique VERDÚ, Victoria SALVADÓ a 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, roč. 14, č. 2, s. 300-311. ISSN 1948-7193. Dostupné z: 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 a 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, roč.~14, č.~2, s.~300-311. ISSN~1948-7193. Dostupné z: https://dx.doi.org/10.1021/acschemneuro.2c00665.

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