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Grup de Recerca Química Analítica i Ambiental

Publication on rapid discrimination of multiple myeloma patients

Victòria Salvadó, together with other authors, has published the paper entitled “Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma” in the SCIENTIFIC REPORTS, a prestigious journal from the Nature publishers. This research has been conducted within the “Biomedicine” research line of the Environmental and Analytical Chemistry research group.

Victòria Salvadó is coauthor of the paper entitled Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma, together with Meritxell Deulofeu1,2,3, Lenka Kolářová4, Eladia María Peña-Méndez6, Martina Almáši7, Martin Štork8, Luděk Pour8, Pere Boadas-Vaello2,3, Sabina Ševčíková9, Josef Havel4,10 and Petr Vaňhara1,10. The paper has been published in the SCIENTIFIC REPORTS, a prestigious journal from the NATURE publishers.

The authors explain that multiple myeloma (MM) is a highly heterogeneous disease of malignant plasma cells. Diagnosis and monitoring of MM patients is based on bone marrow biopsies and detection of abnormal immunoglobulin in serum and/or urine. However, biopsies have a single-site bias; thus, new diagnostic tests and early detection strategies are needed. Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) is a powerful method that found its applications in clinical diagnostics. Artificial intelligence approaches, such as Artificial Neural Networks (ANNs), can handle non-linear data and provide prediction and classification of variables in multidimensional datasets. In this study, the authors used MALDI-TOF MS to acquire low mass profiles of peripheral blood plasma obtained from MM patients and healthy donors. Informative patterns in mass spectra served as inputs for ANN that specifically predicted MM samples with high sensitivity (100%), specificity (95%) and accuracy (98%). Thus, mass spectrometry coupled with ANN can provide a minimally invasive approach for MM diagnostics.

This research has been conducted within the “Biomedicine” research line of the Environmental and Analytical Chemistry research group.

This work was supported by Ministry of Health of the Czech Republic, grants nr. NV18-08-00299 and AZV17- 29343A, the National Program of Sustainability II (Project No. LQ1605, MEYS CR), and by Masaryk University (MUNI/A/1298/2017).

 

The affiliations of the authors are:

1 Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Brno, Czech Republic.

2 Research Group of Clinical Anatomy, Embryology and Neuroscience (NEOMA), Department of Medical Sciences, University of Girona, Girona, Spain.

3 Experimental Neurophysiology and Clinical Anatomy (NE∾ 2017 SGR 01279), Department of Medical Sciences, University of Girona, Girona, Spain.

4 Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czech Republic.

5 Department of Chemistry, Faculty of Science, University of Girona, Girona, Spain.

6 Department of Chemistry, Analytical Chemistry Division, Faculty of Science, University of La Laguna, La Laguna, Spain.

7 Department of Clinical Hematology, University Hospital Brno, Brno, Czech Republic.

8 Department of Internal Medicine, Hematology and Oncology, University Hospital Brno, Brno, Czech Republic.

9 Babak Myeloma Group, Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic.

10 International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic.

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