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Machine Learning Technology for EEG-Forecast of the Blood–Brain Barrier Leakage and the Activation of the Brain’s Drainage System during Isoflurane Anesthesia
Anesthesia enables the painless performance of complex surgical procedures. However, the effects of anesthesia on the brain may not be limited only by its duration. Also, anesthetic agents may cause long-lasting changes in the brain. There is growing evidence that anesthesia can disrupt the integrit...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669477/ https://www.ncbi.nlm.nih.gov/pubmed/38002287 http://dx.doi.org/10.3390/biom13111605 |
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author | Semyachkina-Glushkovskaya, Oxana Sergeev, Konstantin Semenova, Nadezhda Slepnev, Andrey Karavaev, Anatoly Hramkov, Alexey Prokhorov, Mikhail Borovkova, Ekaterina Blokhina, Inna Fedosov, Ivan Shirokov, Alexander Dubrovsky, Alexander Terskov, Andrey Manzhaeva, Maria Krupnova, Valeria Dmitrenko, Alexander Zlatogorskaya, Daria Adushkina, Viktoria Evsukova, Arina Tuzhilkin, Matvey Elizarova, Inna Ilyukov, Egor Myagkov, Dmitry Tuktarov, Dmitry Kurths, Jürgen |
author_facet | Semyachkina-Glushkovskaya, Oxana Sergeev, Konstantin Semenova, Nadezhda Slepnev, Andrey Karavaev, Anatoly Hramkov, Alexey Prokhorov, Mikhail Borovkova, Ekaterina Blokhina, Inna Fedosov, Ivan Shirokov, Alexander Dubrovsky, Alexander Terskov, Andrey Manzhaeva, Maria Krupnova, Valeria Dmitrenko, Alexander Zlatogorskaya, Daria Adushkina, Viktoria Evsukova, Arina Tuzhilkin, Matvey Elizarova, Inna Ilyukov, Egor Myagkov, Dmitry Tuktarov, Dmitry Kurths, Jürgen |
author_sort | Semyachkina-Glushkovskaya, Oxana |
collection | PubMed |
description | Anesthesia enables the painless performance of complex surgical procedures. However, the effects of anesthesia on the brain may not be limited only by its duration. Also, anesthetic agents may cause long-lasting changes in the brain. There is growing evidence that anesthesia can disrupt the integrity of the blood–brain barrier (BBB), leading to neuroinflammation and neurotoxicity. However, there are no widely used methods for real-time BBB monitoring during surgery. The development of technologies for an express diagnosis of the opening of the BBB (OBBB) is a challenge for reducing post-surgical/anesthesia consequences. In this study on male rats, we demonstrate a successful application of machine learning technology, such as artificial neural networks (ANNs), to recognize the OBBB induced by isoflurane, which is widely used in surgery. The ANNs were trained on our previously presented data obtained on the sound-induced OBBB with an 85% testing accuracy. Using an optical and nonlinear analysis of the OBBB, we found that 1% isoflurane does not induce any changes in the BBB, while 4% isoflurane caused significant BBB leakage in all tested rats. Both 1% and 4% isoflurane stimulate the brain’s drainage system (BDS) in a dose-related manner. We show that ANNs can recognize the OBBB induced by 4% isoflurane in 57% of rats and BDS activation induced by 1% isoflurane in 81% of rats. These results open new perspectives for the development of clinically significant bedside technologies for EEG-monitoring of OBBB and BDS. |
format | Online Article Text |
id | pubmed-10669477 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106694772023-11-02 Machine Learning Technology for EEG-Forecast of the Blood–Brain Barrier Leakage and the Activation of the Brain’s Drainage System during Isoflurane Anesthesia Semyachkina-Glushkovskaya, Oxana Sergeev, Konstantin Semenova, Nadezhda Slepnev, Andrey Karavaev, Anatoly Hramkov, Alexey Prokhorov, Mikhail Borovkova, Ekaterina Blokhina, Inna Fedosov, Ivan Shirokov, Alexander Dubrovsky, Alexander Terskov, Andrey Manzhaeva, Maria Krupnova, Valeria Dmitrenko, Alexander Zlatogorskaya, Daria Adushkina, Viktoria Evsukova, Arina Tuzhilkin, Matvey Elizarova, Inna Ilyukov, Egor Myagkov, Dmitry Tuktarov, Dmitry Kurths, Jürgen Biomolecules Article Anesthesia enables the painless performance of complex surgical procedures. However, the effects of anesthesia on the brain may not be limited only by its duration. Also, anesthetic agents may cause long-lasting changes in the brain. There is growing evidence that anesthesia can disrupt the integrity of the blood–brain barrier (BBB), leading to neuroinflammation and neurotoxicity. However, there are no widely used methods for real-time BBB monitoring during surgery. The development of technologies for an express diagnosis of the opening of the BBB (OBBB) is a challenge for reducing post-surgical/anesthesia consequences. In this study on male rats, we demonstrate a successful application of machine learning technology, such as artificial neural networks (ANNs), to recognize the OBBB induced by isoflurane, which is widely used in surgery. The ANNs were trained on our previously presented data obtained on the sound-induced OBBB with an 85% testing accuracy. Using an optical and nonlinear analysis of the OBBB, we found that 1% isoflurane does not induce any changes in the BBB, while 4% isoflurane caused significant BBB leakage in all tested rats. Both 1% and 4% isoflurane stimulate the brain’s drainage system (BDS) in a dose-related manner. We show that ANNs can recognize the OBBB induced by 4% isoflurane in 57% of rats and BDS activation induced by 1% isoflurane in 81% of rats. These results open new perspectives for the development of clinically significant bedside technologies for EEG-monitoring of OBBB and BDS. MDPI 2023-11-02 /pmc/articles/PMC10669477/ /pubmed/38002287 http://dx.doi.org/10.3390/biom13111605 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Semyachkina-Glushkovskaya, Oxana Sergeev, Konstantin Semenova, Nadezhda Slepnev, Andrey Karavaev, Anatoly Hramkov, Alexey Prokhorov, Mikhail Borovkova, Ekaterina Blokhina, Inna Fedosov, Ivan Shirokov, Alexander Dubrovsky, Alexander Terskov, Andrey Manzhaeva, Maria Krupnova, Valeria Dmitrenko, Alexander Zlatogorskaya, Daria Adushkina, Viktoria Evsukova, Arina Tuzhilkin, Matvey Elizarova, Inna Ilyukov, Egor Myagkov, Dmitry Tuktarov, Dmitry Kurths, Jürgen Machine Learning Technology for EEG-Forecast of the Blood–Brain Barrier Leakage and the Activation of the Brain’s Drainage System during Isoflurane Anesthesia |
title | Machine Learning Technology for EEG-Forecast of the Blood–Brain Barrier Leakage and the Activation of the Brain’s Drainage System during Isoflurane Anesthesia |
title_full | Machine Learning Technology for EEG-Forecast of the Blood–Brain Barrier Leakage and the Activation of the Brain’s Drainage System during Isoflurane Anesthesia |
title_fullStr | Machine Learning Technology for EEG-Forecast of the Blood–Brain Barrier Leakage and the Activation of the Brain’s Drainage System during Isoflurane Anesthesia |
title_full_unstemmed | Machine Learning Technology for EEG-Forecast of the Blood–Brain Barrier Leakage and the Activation of the Brain’s Drainage System during Isoflurane Anesthesia |
title_short | Machine Learning Technology for EEG-Forecast of the Blood–Brain Barrier Leakage and the Activation of the Brain’s Drainage System during Isoflurane Anesthesia |
title_sort | machine learning technology for eeg-forecast of the blood–brain barrier leakage and the activation of the brain’s drainage system during isoflurane anesthesia |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669477/ https://www.ncbi.nlm.nih.gov/pubmed/38002287 http://dx.doi.org/10.3390/biom13111605 |
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