Novel Mass Spectrometric Utilities for Assisting in Oncological Surgery

Ivanov, Daniil G.; Pekov, Stanislav I.; Bocharov, K. V.; Bormotov, Denis S.; Spasskiy, A. I.; Zhvansky, Evgeny; Sorokin, Anatoly; Eliferov, Vasiliy A.; Zavorotnyuk, Denis S.; Ткаченко, С. И.; Khaliullin, Ilyas G.; Kuksin, A. Yu.; Shurkhay, Vsevolod; Kononikhin, Alexey; Nikolaev, Evgeny N.; Popov, Igor

doi:10.1134/s1990793120030173

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…It was shown that the molecular profiles of unmodified and damaged brain tissue are separable-various necrotized (necrotized tumor, necrotic tissue with necrotized vessels, necrotic tissue with tumor strain) and tumor (histologically pure tumor, tumor with necrosis, tumor lesions) tissues could be differentiated from each other as well as from the tumor boundary tissues [25]. The same data was further implemented to create classifiers for rapid identification of various tumors (glioblastoma, astrocytoma, meningioma) based on ambient mass spectrometry [34][35][36], which has become the basis for developing new ambient ionization techniques designed for clinical application [10]. On the other hand, the presented dataset, as it is an example of data representing actual data obtained in a clinic, was used as a model for developing an instrument for an interactive and automated tool for evaluating the stability and reproducibility of mass spectra [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], and for the unification of representations of high-and low-resolution mass spectra for further clinical implementation [23].…”

Section: User Notesmentioning

confidence: 99%

“…The same data was further implemented to create classifiers for rapid identification of various tumors (glioblastoma, astrocytoma, meningioma) based on ambient mass spectrometry [34][35][36], which has become the basis for developing new ambient ionization techniques designed for clinical application [10]. On the other hand, the presented dataset, as it is an example of data representing actual data obtained in a clinic, was used as a model for developing an instrument for an interactive and automated tool for evaluating the stability and reproducibility of mass spectra [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37], and for the unification of representations of high-and low-resolution mass spectra for further clinical implementation [23].…”

Section: User Notesmentioning

confidence: 99%

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Lipid Profiles of Human Brain Tumors Obtained by High-Resolution Negative Mode Ambient Mass Spectrometry

Zavorotnyuk

Pekov

Sorokin

et al. 2021

Data

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Alterations in cell metabolism, including changes in lipid composition occurring during malignancy, are well characterized for various tumor types. However, a significant part of studies that deal with brain tumors have been performed using cell cultures and animal models. Here, we present a dataset of 124 high-resolution negative ionization mode lipid profiles of human brain tumors resected during neurosurgery. The dataset is supplemented with 38 non-tumor pathological brain tissue samples resected during elective surgery. The change in lipid composition alterations of brain tumors enables the possibility of discriminating between malignant and healthy tissues with the implementation of ambient mass spectrometry. On the other hand, the collection of clinical samples allows the comparison of the metabolism alteration patterns in animal models or in vitro models with natural tumor samples ex vivo. The presented dataset is intended to be a data sample for bioinformaticians to test various data analysis techniques with ambient mass spectrometry profiles, or to be a source of clinically relevant data for lipidomic research in oncology.

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Section: User Notesmentioning

confidence: 99%

Section: User Notesmentioning

confidence: 99%

Lipid Profiles of Human Brain Tumors Obtained by High-Resolution Negative Mode Ambient Mass Spectrometry

Zavorotnyuk

Pekov

Sorokin

et al. 2021

Data

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“…The method of IR Fourier (FTIR) spectroscopy is the most common method for studying substances in the gas phase both under laboratory and natural conditions [11]. The leading positions in the sensitivity of identification of substances are taken by mass spectrometry methods [12], which is extremely important, for example, for biomedical applications; however, the need for sample preparation and the significant time for obtaining the spectrum do not allow the use of mass spectrometers for solving problems of the express analysis of substances in the gas phase.…”

Section: Introductionmentioning

confidence: 99%

Temperature Dependence of the Sensitivity of an Infrared Fourier Spectrometer

Fufurin

Shlygin²,

Pozvonkov³

et al. 2021

Russ. J. Phys. Chem. B

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The analytical dependence of the signal-to-noise ratio (SNR) of the measured interferograms of an infrared (IR) Fourier spectrometer on the temperature contrast of the observation path and on the physicochemical properties of the test substance is obtained. The analytical dependences of the minimum detectable concentration of a substance on the value of the temperature contrast are obtained. It is shown experimentally that the theoretical estimates of the minimum detectable concentrations with an accuracy of the order of the mean square error correspond to the experimental values. The obtained analytical dependences make it possible for the given physicochemical properties of substances, the parameters of the IR Fourier spectrometer, and the values of the temperature contrast to estimate minimum detectable concentration of the substance.

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Modern machine‐learning applications in ambient ionization mass spectrometry

Sorokin,

Pekov,

Zavorotnyuk

et al. 2024

Mass Spectrometry Reviews

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This article provides a comprehensive overview of the applications of methods of machine learning (ML) and artificial intelligence (AI) in ambient ionization mass spectrometry (AIMS). AIMS has emerged as a powerful analytical tool in recent years, allowing for rapid and sensitive analysis of various samples without the need for extensive sample preparation. The integration of ML/AI algorithms with AIMS has further expanded its capabilities, enabling enhanced data analysis. This review discusses ML/AI algorithms applicable to the AIMS data and highlights the key advancements and potential benefits of utilizing ML/AI in the field of mass spectrometry, with a focus on the AIMS community.

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Novel Mass Spectrometric Utilities for Assisting in Oncological Surgery

Cited by 8 publications

References 36 publications

Lipid Profiles of Human Brain Tumors Obtained by High-Resolution Negative Mode Ambient Mass Spectrometry

Lipid Profiles of Human Brain Tumors Obtained by High-Resolution Negative Mode Ambient Mass Spectrometry

Temperature Dependence of the Sensitivity of an Infrared Fourier Spectrometer

Modern machine‐learning applications in ambient ionization mass spectrometry

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