2017
Diagnosis of Multiple Myeloma by Mass Spectrometry of Peripheral Blood Plasma and Artificial Intelligence
DEULOFEU FIGUERAS, Meritxell, Lenka KOLÁŘOVÁ, Victoria SALVADO, Eladia Maria PEÑA-MÉNDEZ, Pere BODAS-VAELLO et. al.Základní údaje
Originální název
Diagnosis of Multiple Myeloma by Mass Spectrometry of Peripheral Blood Plasma and Artificial Intelligence
Název anglicky
Diagnosis of Multiple Myeloma by Mass Spectrometry of Peripheral Blood Plasma and Artificial Intelligence
Autoři
DEULOFEU FIGUERAS, Meritxell, Lenka KOLÁŘOVÁ, Victoria SALVADO, Eladia Maria PEÑA-MÉNDEZ, Pere BODAS-VAELLO, Luděk POUR, Sabina ŠEVČÍKOVÁ, Martina ALMÁŠI, Aleš HAMPL, Petr VAŇHARA a Josef HAVEL
Vydání
MSACL 2017 EU; The 4th Annual European Congress of The Association for Mass Spectrometry: Applications to the Clinical Lab. 2017
Další údaje
Typ výsledku
Prezentace na konferencích
Utajení
není předmětem státního či obchodního tajemství
Změněno: 24. 12. 2017 10:56, Mgr. Ing. Lubomír Prokeš, Ph.D.
V originále
A fast and simple method for the diagnosis of multiple myeloma by the analysis of peripheral blood plasma mass spectra has been developed. It is based on recording the Matrix Assisted Laser Desorption Ionisation Time Of Flight (MALDI TOF) mass spectra of low mass metabolites/compounds (below 2000 Daltons) and the evaluation of these data using Artificial Neural Networks (ANNs). The method, which does not require the identification of biomarkers, has been verified using clinical database of myeloma positive and negative patients.
Anglicky
A fast and simple method for the diagnosis of multiple myeloma by the analysis of peripheral blood plasma mass spectra has been developed. It is based on recording the Matrix Assisted Laser Desorption Ionisation Time Of Flight (MALDI TOF) mass spectra of low mass metabolites/compounds (below 2000 Daltons) and the evaluation of these data using Artificial Neural Networks (ANNs). The method, which does not require the identification of biomarkers, has been verified using clinical database of myeloma positive and negative patients.