DEULOFEU FIGUERAS, Meritxell, Lenka KOLÁŘOVÁ, Victoria SALVADO, Eladia Maria PEÑA-MÉNDEZ, Pere BODAS-VAELLO, Luděk POUR, Sabina ŠEVČÍKOVÁ, Martina ALMÁŠI, Aleš HAMPL, Petr VAŇHARA and Josef HAVEL. Diagnosis of Multiple Myeloma by Mass Spectrometry of Peripheral Blood Plasma and Artificial Intelligence. In MSACL 2017 EU; The 4th Annual European Congress of The Association for Mass Spectrometry: Applications to the Clinical Lab. 2017.
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Basic information
Original name Diagnosis of Multiple Myeloma by Mass Spectrometry of Peripheral Blood Plasma and Artificial Intelligence
Name (in English) Diagnosis of Multiple Myeloma by Mass Spectrometry of Peripheral Blood Plasma and Artificial Intelligence
Authors DEULOFEU FIGUERAS, Meritxell, Lenka KOLÁŘOVÁ, Victoria SALVADO, Eladia Maria PEÑA-MÉNDEZ, Pere BODAS-VAELLO, Luděk POUR, Sabina ŠEVČÍKOVÁ, Martina ALMÁŠI, Aleš HAMPL, Petr VAŇHARA and Josef HAVEL.
Edition MSACL 2017 EU; The 4th Annual European Congress of The Association for Mass Spectrometry: Applications to the Clinical Lab. 2017.
Other information
Type of outcome Presentations at conferences
Confidentiality degree is not subject to a state or trade secret
Changed by Changed by: Mgr. Ing. Lubomír Prokeš, Ph.D., učo 18302. Changed: 24/12/2017 10:56.
Abstract
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.
Abstract (in English)
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.
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