k 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.

Anotace

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.