ISSN Print 2500–1094
ISSN Online 2542–1204
BIOMEDICAL JOURNAL OF PIROGOV UNIVERSITY (MOSCOW, RUSSIA)
ORIGINAL RESEARCH
Determining optimal ambient ionization mass spectrometry data pre-processing parameters in neurosurgery
About authors
1 Moscow Institute of Physics and Technology, Moscow, Russia
2 Semenov Federal Research Center for Chemical Physics of the Russian Academy of Sciences, Moscow, Russia
3 Skolkovo Institute of Science and Technology, Moscow, Russia
4 Siberian State Medical University, Tomsk, Russia
Correspondence should be addressed: Denis S. Zavorotnyuk
Institutskiy per., 9, str. 7, Dolgoprudny, Moscow Region, 141701; moc.liamg@kuyntorovaz.sined
About paper
Funding: the study was performed within the framework of the state assignment of the Ministry of Science and Higher Education of the Russian Federation (agreement № 075-03-2022-107, project № 0714-2020-0006). The study involved the use of equipment of the Semenov Federal Research Center for Chemical Physics RAS.
Author contribution: Zavorotnyuk DS — data acquisition and interpretation, software development, manuscript writing and editing; Sorokin AA — study planning, data analysis and interpretation, manuscript editing; Bormotov DS — data acquisition and interpretation, manuscript writing; Eliferov VA — financial support of the experiment; Bocharov KV — data acquisition; Pekov SI — study planning, data analysis and interpretation, manuscript draft writing and manuscript text finalization; Popov IA — project management, financial support.
Compliance with ethical standards: the study was approved by the Ethics Committee of the Burdenko Research Institute of Neurosurgery (protocols № 40 dated 12 April 2016 and № 131 dated 17 July 2018) and conducted in accordance with the principles of the Declaration of Helsinki (2000) and its subsequent revisions. All patients submitted the informed consent to study participation and the use of biomaterial for scientific purposes.
Received: 2024-12-19
Accepted: 2024-03-03
Published online: 2024-04-27
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Radical tumor resection is still the most effective treatment method for brain tumors. The problems of intraoperative monitoring are currently solved using positron emission tomography, magnetic resonance imaging, and histochemical analysis, however, these require using expensive equipment by highly qualified personnel and are therefore still not widely available. As an alternative, it is possible to use mass spectrometry methods without sample preparation and then the analysis of mass spectrometry data involving the use of machine learning methods. The spectra that are more rich and diverse in terms of peak number are typical for mass spectrometry without sample preparation, therefore the use of this method requires specific pre-processing of experimental data. The study was aimed to develop the methods to determine the optimal parameter values for pre-processing of the data acquired by ambient ionization mass spectrometry. The paper presents two such methods and provides specific parameter values for the data acquired using the Thermo LTQ XL Orbitrap ETD mass spectrometer.
Keywords: mass spectrometry, ambient ionization, data analysis, data preprocessing