Mass spectometry coupled with artificial neural networks for discrimination of extramedullary multiple myeloma

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F65269705%3A_____%2F21%3A00075537" target="_blank" >RIV/65269705:_____/21:00075537 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.prolekare.cz/casopisy/transfuze-hematologie-dnes/archiv-cisel/2021-supplementum-2" target="_blank" >https://www.prolekare.cz/casopisy/transfuze-hematologie-dnes/archiv-cisel/2021-supplementum-2</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Mass spectometry coupled with artificial neural networks for discrimination of extramedullary multiple myeloma

Popis výsledku v původním jazyce

Introduction: Multiple myeloma (MM) is the second most common hematological malignancy of the elderly. The bone marrow is infiltrated by malignant plasma cells. MM may progress into so-called extramedullary disease (EMD). EMD occurs when a subclone of clonal plasma cells migrates out of the bone marrow and infiltrates soft tissues. Aim: We focused on the analysis of low molecular weight molecules in peripheral blood of 20 MM and 20 EMD patients using MALDI-TOF mass spectrometry to create a dia gnostic tool based on prediction by artificial neural network, which should distinguish diff erent groups of diseases. Methods: Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) has become an indispensable research tool, which is used for analysis of bio molecules and various organic molecules. Artificial Neural Networks (ANN) are components of artifi cial intelligence inspired by bio logical neural networks. Using ANN, we can model complex non-linear systems, as previously published. In our previous study, we recorded mass spectra of MM and healthy donor samples. ANN specifi cally predicted MM samples with high sensitivity, specifi city and accuracy. Results: The RStudio was used for statistical analysis, where the data were evaluated using Principal Component Analysis (PCA) and Partial least squares discriminant analysis (PSL-DA). Using MALDI-TOF MS, it was possible to distinguish between samples of MM patients and healthy donors, as well as MM and EMD patients. Informative patterns in mass spectra served as inputs for ANN that specifically distinguished between healthy donors and patients. Conclusion: We demonstrated that using MALDI-TOF MS coupled with ANN is a useful tool that can distinguish between healthy donors and patients. Thus, it can be used as a fast alternative to conventional analyses.

Název v anglickém jazyce

Mass spectometry coupled with artificial neural networks for discrimination of extramedullary multiple myeloma

Popis výsledku anglicky

Introduction: Multiple myeloma (MM) is the second most common hematological malignancy of the elderly. The bone marrow is infiltrated by malignant plasma cells. MM may progress into so-called extramedullary disease (EMD). EMD occurs when a subclone of clonal plasma cells migrates out of the bone marrow and infiltrates soft tissues. Aim: We focused on the analysis of low molecular weight molecules in peripheral blood of 20 MM and 20 EMD patients using MALDI-TOF mass spectrometry to create a dia gnostic tool based on prediction by artificial neural network, which should distinguish diff erent groups of diseases. Methods: Matrix-Assisted Laser Desorption/Ionization Time-of Flight Mass Spectrometry (MALDI-TOF MS) has become an indispensable research tool, which is used for analysis of bio molecules and various organic molecules. Artificial Neural Networks (ANN) are components of artifi cial intelligence inspired by bio logical neural networks. Using ANN, we can model complex non-linear systems, as previously published. In our previous study, we recorded mass spectra of MM and healthy donor samples. ANN specifi cally predicted MM samples with high sensitivity, specifi city and accuracy. Results: The RStudio was used for statistical analysis, where the data were evaluated using Principal Component Analysis (PCA) and Partial least squares discriminant analysis (PSL-DA). Using MALDI-TOF MS, it was possible to distinguish between samples of MM patients and healthy donors, as well as MM and EMD patients. Informative patterns in mass spectra served as inputs for ANN that specifically distinguished between healthy donors and patients. Conclusion: We demonstrated that using MALDI-TOF MS coupled with ANN is a useful tool that can distinguish between healthy donors and patients. Thus, it can be used as a fast alternative to conventional analyses.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

30205 - Hematology

Projekt

<a href="/cs/project/NV18-03-00203" target="_blank" >NV18-03-00203: Tekuté biopsie u plazmocelulární leukemie</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů