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74 results on '"Chagovets, Vitaliy"'

53. Combination of Low-Temperature Electrosurgical Unit and Extractive Electrospray Ionization Mass Spectrometry for Molecular Profiling and Classification of Tissues

Author

Sukhikh, Gennady, primary, Chagovets, Vitaliy, additional, Wang, Xinchen, additional, Rodionov, Valeriy, additional, Kometova, Vlada, additional, Tokareva, Alisa, additional, Kononikhin, Alexey, additional, Starodubtseva, Natalia, additional, Chingin, Konstantin, additional, Chen, Huanwen, additional, and Frankevich, Vladimir, additional

Published
2019
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54. Label-free cervicovaginal fluid proteome profiling reflects the cervix neoplastic transformation

Author

Starodubtseva, Natalia L., primary, Brzhozovskiy, Alexander G., additional, Bugrova, Anna E., additional, Kononikhin, Alexey S., additional, Indeykina, Maria I., additional, Gusakov, Kiril I., additional, Chagovets, Vitaliy V., additional, Nazarova, Niso M., additional, Frankevich, Vladimir E., additional, Sukhikh, Gennady T., additional, and Nikolaev, Eugene N., additional

Published
2019
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56. Relative quantitation of phosphatidylcholines with interfered masses of protonated and sodiated molecules by tandem and Fourier-transform ion cyclotron resonance mass spectrometry

Author

Chagovets, Vitaliy, primary, Kononikhin, Aleksey, additional, Tokoreva, Alisa, additional, Bormotov, Denis, additional, Starodubtseva, Nataliia, additional, Kostyukevich, Yury, additional, Popov, Igor, additional, Frankevich, Vladimir, additional, and Nikolaev, Eugene, additional

Published
2019
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58. Corrigendum to “Identification of potential early biomarkers of preeclampsia” [Placenta (2018) 61–71]

Author

Timofeeva, Angelika V., primary, Gusar, Vladyslava A., additional, Kan, Nataliya E., additional, Prozorovskaya, Kseniya N., additional, Karapetyan, Anna O., additional, Bayev, Oleg R., additional, Chagovets, Vitaliy V., additional, Kliver, Sergei F., additional, Iakovishina, Daria Yu., additional, Frankevich, Vladimir E., additional, and Sukhikh, Gennadiy T., additional

Published
2018
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59. Identification of potential early biomarkers of preeclampsia

Author

Timofeeva, Angelika V., primary, Gusar, Vladyslava A., additional, Kan, Nataliya E., additional, Prozorovskaya, Kseniya N., additional, Karapetyan, Anna O., additional, Bayev, Oleg R., additional, Chagovets, Vitaliy V., additional, Kliver, Sergei F., additional, Iakovishina, Daria Yu., additional, Frankevich, Vladimir E., additional, and Sukhikh, Gennadiy T., additional

Published
2018
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60. A Comparison of Tissue Spray and Lipid Extract Direct Injection Electrospray Ionization Mass Spectrometry for the Differentiation of Eutopic and Ectopic Endometrial Tissues

Author

Chagovets, Vitaliy, primary, Wang, Zhihao, additional, Kononikhin, Alexey, additional, Starodubtseva, Natalia, additional, Borisova, Anna, additional, Salimova, Dinara, additional, Popov, Igor, additional, Kozachenko, Andrey, additional, Chingin, Konstantin, additional, Chen, Huanwen, additional, Frankevich, Vladimir, additional, Adamyan, Leila, additional, and Sukhikh, Gennady, additional

Published
2017
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61. Endometriosis foci differentiation by rapid lipid profiling using tissue spray ionization and high resolution mass spectrometry

Author

Chagovets, Vitaliy V., primary, Wang, Zhihao, additional, Kononikhin, Alexey S., additional, Starodubtseva, Natalia L., additional, Borisova, Anna, additional, Salimova, Dinara, additional, Popov, Igor A., additional, Kozachenko, Andrey V., additional, Chingin, Konstantin, additional, Chen, Huanwen, additional, Frankevich, Vladimir E., additional, Adamyan, Leila V., additional, and Sukhikh, Gennady T., additional

Published
2017
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63. Molecular analysis of semen-like odor emitted by chestnut flowers using neutral desorption extractive atmospheric pressure chemical ionization mass spectrometry.

Author

Zhang, Xiaoping, Ji, Yin, Zhang, Yi, Liu, Fen, Chen, Honghan, Liu, Jianchuan, Handberg, Eric Storr, Chagovets, Vitaliy V., and Chingin, Konstantin

Subjects
CHEMICAL ionization mass spectrometry, ATMOSPHERIC pressure, CHESTNUT, CASTANEA, ODORS, FLOWERS
Abstract

Knowledge about the chemical composition of floral volatile organic compounds (VOCs) is valuable in biological studies as well as for the flavor, cosmetic, and fragrance industries. The flowers of Chinese chestnut (Castanea mollissima) emit a distinctive semen-like odor; however, the chemical composition and biological role for the semen-like odor of chestnut flowers remain scarcely studied. Herein, we report the floral VOCs and the pollinators of chestnut flowers. A fast method based on a neutral desorption (ND) device coupled to extractive atmospheric pressure chemical ionization mass spectrometry (EAPCI-MS) was developed for the rapid identification of VOCs from freshly collected chestnut flowers without any chemical pretreatment. Chemical identification was performed using high-resolution MS analysis in combination with tandem MS analysis and whenever possible was confirmed by the analysis of standard reference compounds. Twenty volatiles were identified, most of which are nitrogen-containing. Out of the identified volatiles, 1-pyrroline is known to have a semen-like odor and is probably also responsible for the semen-like odor of the chestnut flowers. Four nitrogenous VOCs of chestnut flowers, including 1-pyrroline, 1-piperideine, 2-pyrrolidone, and phenethylamine, were also common in other semen-like odor flowers such as Photinia serrulata, Castanopsis sclerophylla, and Stemona japonica, suggesting similar chemical origin. The main visitors of chestnut flowers were dipteran species, such as Eristalis tenax, Eristalis arvorum, Episyrphus balteatus, Lucilia sericata, Chrysomya megacephala, Chrysochus asclepiadeus, and Adalia bipunctata. Our results suggest that the chestnut flowers and other semen-like odor flowers may present a new type of sapromyophily. This study also indicates that ND-EAPCI-MS provides more sensitive and simpler detection of many VOCs (particularly nitrogen-containing VOCs) than GC-MS and therefore can be used to complement traditional approaches for the higher chemical coverage of VOCs analysis. [ABSTRACT FROM AUTHOR]

Published
2019
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69. Noncovalent Interaction of Methylene Blue with Carbon Nanotubes: Theoretical and Mass Spectrometry Characterization

Author

Chagovets, Vitaliy V., primary, Kosevich, Marina V., additional, Stepanian, Stepan G., additional, Boryak, Oleg A., additional, Shelkovsky, Vadim S., additional, Orlov, Vadim V., additional, Leontiev, Victor S., additional, Pokrovskiy, Valerij A., additional, Adamowicz, Ludwik, additional, and Karachevtsev, Victor A., additional

Published
2012
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72. Feature selection for OPLS discriminant analysis of cancer tissue lipidomics data.

Author

Tokareva, Alisa O., Chagovets, Vitaliy V., Starodubtseva, Natalia L., Nazarova, Niso M., Nekrasova, Maria E., Kononikhin, Alexey S., Frankevich, Vladimir E., Nikolaev, Evgeny N., and Sukhikh, Gennady T.

Subjects
*FEATURE selection, *DISCRIMINANT analysis, *RANDOM forest algorithms, *LATENT structure analysis, *TISSUE analysis, *MANN Whitney U Test
Abstract

The mass spectrometry‐based molecular profiling can be used for better differentiation between normal and cancer tissues and for the detection of neoplastic transformation, which is of great importance for diagnostics of a pathology, prognosis of its evolution trend, and development of a treatment strategy. The aim of the present study is the evaluation of tissue classification approaches based on various data sets derived from the molecular profile of the organic solvent extracts of a tissue. A set of possibilities are considered for the orthogonal projections to latent structures discriminant analysis: all mass spectrometric peaks over 300 counts threshold, subset of peaks selected by ranking with support vector machine algorithm, peaks selected by random forest algorithm, peaks with the statistically significant difference of the intensity determined by the Mann‐Whitney U test, peaks identified as lipids, and both identified and significantly different peaks. The best predictive potential is obtained for OPLS‐DA model built on nonpolar glycerolipids (Q2 = 0.64, area under curve [AUC] = 0.95); the second one is OPLS‐DA model with lipid peaks selected by random forest algorithm (Q2 = 0.58, AUC = 0.87). Moreover, models based on particular molecular classes are more preferable from biological point of view, resulting in new explanatory mechanisms of pathophysiology and providing a pathway analysis. Another promising features for OPLS‐DA modeling are phosphatidylethanolamines (Q2 = 0.48, AUC = 0.86). [ABSTRACT FROM AUTHOR]

Published
2020
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73. Methodology for Urine Peptidome Analysis Based on Nano-HPLC Coupled to Fourier Transform Ion Cyclotron Resonance Mass Spectrometry.

Author

Kononikhin AS, Sergeeva VA, Bugrova AE, Indeykina MI, Starodubtseva NL, Chagovets VV, Popov IA, Frankevich VE, Pedrioli P, Sukhikh GT, and Nikolaev EN

Subjects
Humans, Chromatography, High Pressure Liquid methods, Cyclotrons instrumentation, Peptide Fragments urine, Spectrometry, Mass, Electrospray Ionization methods, Spectroscopy, Fourier Transform Infrared methods, Urinalysis methods
Abstract

Urine is a sample of choice for noninvasive biomarkers search because it is easily available in large amounts and its molecular composition provides information on processes in the organism. The high potential of urine peptidomics has been demonstrated for clinical purpose. Several mass spectrometry based approaches have been successfully applied for urine peptidome analysis and potential biomarkers search. Summarizing literature data and our own experience we developed a protocol for comprehensive urine peptidome analysis. The technology includes several stages and consists of urine sample preparation by size exclusion chromatography and identification of featured peptides by nano-HPLC coupled to Fourier transform ion cyclotron resonance mass spectrometry, semiquantitative and statistical data analysis.

Published
2018
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74. Letter: Comparison of pyridine and pyrazine derivatives distributions in exhaled breath and exhaled breath condensate after smoking.

Author

Chagovets V, Kononikhin A, Starodubtseva N, Ding J, Chen H, and Frankevich V

Subjects
Humans, Reproducibility of Results, Sensitivity and Specificity, Breath Tests methods, Exhalation physiology, Pyrazines analysis, Pyridines analysis, Smoking metabolism, Spectrometry, Mass, Electrospray Ionization methods
Abstract

Molecular mass distributions of series of compounds found in exhaled breath after smoking are compared between the direct-breath analysis by extractive electrospray ionization and the analysis of exhaled breath condensate by direct-infusion electrospray ionization mass spectrometry. Although all the analyzed series of compounds are detected by both methods, their relative abundances are different. A number-average mass is used as a quantitative characteristic of the series. It is shown that this value is close for the distributions of the series of compounds in the mass spectrum of exhaled breath condensate and the mass spectrum obtained by summation of direct mass spectra over the breathing time.

Published
2015
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