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Discriminating fingerprints of chronic neuropathic pain following spinal cord injury using artificial neural networks and mass spectrometry analysis of female mice serum
M. Deulofeu, EM. Peña-Méndez, P. Vaňhara, J. Havel, L. Moráň, L. Pečinka, A. Bagó-Mas, E. Verdú, V. Salvadó, P. Boadas-Vaello
Jazyk angličtina Země Anglie, Velká Británie
Typ dokumentu časopisecké články
- MeSH
- biologické markery krev MeSH
- chronická bolest krev diagnóza etiologie MeSH
- myši inbrední C57BL MeSH
- myši MeSH
- neuralgie * krev diagnóza etiologie MeSH
- neuronové sítě (počítačové) * MeSH
- poranění míchy * komplikace krev MeSH
- spektrometrie hmotnostní - ionizace laserem za účasti matrice * metody MeSH
- zvířata MeSH
- Check Tag
- myši MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
Spinal cord injury (SCI) often leads to central neuropathic pain, a condition associated with significant morbidity and is challenging in terms of the clinical management. Despite extensive efforts, identifying effective biomarkers for neuropathic pain remains elusive. Here we propose a novel approach combining matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with artificial neural networks (ANNs) to discriminate between mass spectral profiles associated with chronic neuropathic pain induced by SCI in female mice. Functional evaluations revealed persistent chronic neuropathic pain following mild SCI as well as minor locomotor disruptions, confirming the value of collecting serum samples. Mass spectra analysis revealed distinct profiles between chronic SCI and sham controls. On applying ANNs, 100% success was achieved in distinguishing between the two groups through the intensities of m/z peaks. Additionally, the ANNs also successfully discriminated between chronic and acute SCI phases. When reflexive pain response data was integrated with mass spectra, there was no improvement in the classification. These findings offer insights into neuropathic pain pathophysiology and underscore the potential of MALDI-TOF MS coupled with ANNs as a diagnostic tool for chronic neuropathic pain, potentially guiding attempts to discover biomarkers and develop treatments.
Department of Chemistry Faculty of Science University of Girona 17071 Girona Catalonia Spain
International Clinical Research Center St Anne's University Hospital 656 91 Brno Czech Republic
Research Centre for Applied Molecular Oncology Masaryk Memorial Cancer Institute Brno Czech Republic
Citace poskytuje Crossref.org
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- $a Deulofeu, Meritxell $u Research Group of Clinical Anatomy, Embryology and Neuroscience (NEOMA), Department of Medical Sciences, University of Girona, Girona, Catalonia, Spain; Department of Chemistry, Faculty of Science, Masaryk University, Kamenice 5/A14, 625 00, Brno, Czech Republic; Department of Histology and Embryology, Faculty of Medicine, Masaryk University, 62500, Brno, Czech Republic
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- $a Spinal cord injury (SCI) often leads to central neuropathic pain, a condition associated with significant morbidity and is challenging in terms of the clinical management. Despite extensive efforts, identifying effective biomarkers for neuropathic pain remains elusive. Here we propose a novel approach combining matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with artificial neural networks (ANNs) to discriminate between mass spectral profiles associated with chronic neuropathic pain induced by SCI in female mice. Functional evaluations revealed persistent chronic neuropathic pain following mild SCI as well as minor locomotor disruptions, confirming the value of collecting serum samples. Mass spectra analysis revealed distinct profiles between chronic SCI and sham controls. On applying ANNs, 100% success was achieved in distinguishing between the two groups through the intensities of m/z peaks. Additionally, the ANNs also successfully discriminated between chronic and acute SCI phases. When reflexive pain response data was integrated with mass spectra, there was no improvement in the classification. These findings offer insights into neuropathic pain pathophysiology and underscore the potential of MALDI-TOF MS coupled with ANNs as a diagnostic tool for chronic neuropathic pain, potentially guiding attempts to discover biomarkers and develop treatments.
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