Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Sergey Osipenko"'
Autor:
Yury Kostyukevich, Sergey Sosnin, Sergey Osipenko, Oxana Kovaleva, Lidiia Rumiantseva, Albert Kireev, Alexander Zherebker, Maxim Fedorov, Evgeny N. Nikolaev
Publikováno v:
ACS Omega, Vol 7, Iss 11, Pp 9710-9719 (2022)
Externí odkaz:
https://doaj.org/article/a9b346370723442b8f2b2f42c8360157
Autor:
Sergey Osipenko, Anton Bashilov, Anna Vishnevskaya, Lidiia Rumiantseva, Anna Levashova, Anna Kovalenko, Boris Tupertsev, Albert Kireev, Eugene Nikolaev, Yury Kostyukevich
Publikováno v:
International Journal of Molecular Sciences, Vol 24, Iss 20, p 15396 (2023)
Mass spectrometry has been an essential technique for the investigation of the metabolic pathways of living organisms since its appearance at the beginning of the 20th century. Due to its capability to resolve isotopically labeled species, it can be
Externí odkaz:
https://doaj.org/article/b8fd9d1ffc544a5abf0ae9d7c6639183
Autor:
Yury Kostyukevich, Elena Stekolshikova, Anna Levashova, Anna Kovalenko, Anna Vishnevskaya, Anton Bashilov, Albert Kireev, Boris Tupertsev, Lidiia Rumiantseva, Philipp Khaitovich, Sergey Osipenko, Eugene Nikolaev
Publikováno v:
International Journal of Molecular Sciences, Vol 24, Iss 14, p 11725 (2023)
The administration of low doses of D2O to living organisms was used for decades for the investigation of metabolic pathways and for the measurement of the turnover rate for specific compounds. Usually, the investigation of the deuterium uptake in lip
Externí odkaz:
https://doaj.org/article/cf8931483cbd4dacb3f3b9e52619292c
Publikováno v:
International Journal of Molecular Sciences, Vol 24, Iss 5, p 4569 (2023)
The identification of drug metabolites formed with different in vitro systems by HPLC-MS is a standard step in preclinical research. In vitro systems allow modeling of real metabolic pathways of a drug candidate. Despite the emergence of various soft
Externí odkaz:
https://doaj.org/article/eb73aa40e5eb4068bf063b718757b208
Publikováno v:
Separations, Vol 9, Iss 10, p 291 (2022)
Retention time prediction, facilitated by advances in machine learning, has become a useful tool in untargeted LC-MS applications. State-of-the-art approaches include graph neural networks and 1D-convolutional neural networks that are trained on the
Externí odkaz:
https://doaj.org/article/0c369728bd3c42b0b4572f498ac37c07
Publikováno v:
Separations, Vol 9, Iss 10, p 265 (2022)
During on-site verification activities conducted by the Technical Secretariat of Organization for the Prohibition of Chemical Weapons, identification by gas chromatography retention indices (RI) data, in addition to mass spectrometry data, increase t
Externí odkaz:
https://doaj.org/article/2615e6fb8eb64e6e83a07a73306e6e56
Autor:
Anton Bashilov, Sergey Osipenko, Karolina Ikonnikova, Oxana Kovaleva, Boris Izotov, Evgeny Nikolaev, Yury Kostyukevich
Publikováno v:
Separations, Vol 9, Iss 9, p 250 (2022)
The role of phosphatidylethanol (PEth) as an alcohol consumption marker is increasing in clinical and forensic medicine. During the COVID-19 pandemic, the role of hygiene increased, and it became common practice to use disinfectants almost everywhere
Externí odkaz:
https://doaj.org/article/5c496366cec645b5807787a1e679be6b
Autor:
Lidiia Rumiantseva, Sergey Osipenko, Artem Zharikov, Albert Kireev, Evgeny N. Nikolaev, Yury Kostyukevich
Publikováno v:
International Journal of Molecular Sciences, Vol 23, Iss 7, p 3585 (2022)
Mono- and polysaccharides are an essential part of every biological system. Identifying underivatized carbohydrates using mass spectrometry is still a challenge because carbohydrates have a low capacity for ionization. Normally, the intensities of pr
Externí odkaz:
https://doaj.org/article/0a35ee41e67743e1aa4f0a8942faf129
Autor:
Lidiia Rumiantseva, Sergey Osipenko, Ilya I. Podolskiy, Dmitry A. Burmykin, Oxana Kovaleva, Evgeny N. Nikolaev, Yury Kostyukevich
Publikováno v:
Analytical and Bioanalytical Chemistry. 414:2537-2543
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7bae41cdd25927b13b73a9f04d83b0f9
https://doi.org/10.1007/s00216-022-03924-9
https://doi.org/10.1007/s00216-022-03924-9
Autor:
Mark Zaretckii, Inga Bashkirova, Sergey Osipenko, Yury Kostyukevich, Evgeny Nikolaev, Petr Popov
Publikováno v:
Digital Discovery. 1:711-718
We present a robust deep learning method CPORT to predict retention time from 3D molecular structures. It generates 4D tensor representations of 3D conformers, that are processed by a neural network with 3D convolutional and fully-connected layers.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::607639209762832549d48697fc27537f
https://doi.org/10.1039/d2dd00021k
https://doi.org/10.1039/d2dd00021k