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Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review

The physiological and neuroregulatory mechanism of propofol is largely based on very limited knowledge. It is one of the important puzzling issues in anesthesiology and is of great value in both scientific and clinical fields. It is acknowledged that neural networks which are comprised of a number o...

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Detalles Bibliográficos
Autores principales: Li, Zhen, Liu, Jia, Liang, Huazheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9129930/
https://www.ncbi.nlm.nih.gov/pubmed/35619772
http://dx.doi.org/10.1155/2022/8202869
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author Li, Zhen
Liu, Jia
Liang, Huazheng
author_facet Li, Zhen
Liu, Jia
Liang, Huazheng
author_sort Li, Zhen
collection PubMed
description The physiological and neuroregulatory mechanism of propofol is largely based on very limited knowledge. It is one of the important puzzling issues in anesthesiology and is of great value in both scientific and clinical fields. It is acknowledged that neural networks which are comprised of a number of neural circuits might be involved in the anesthetic mechanism. However, the mechanism of this hypothesis needs to be further elucidated. With the progress of artificial intelligence, it is more likely to solve this problem through using artificial neural networks to perform temporal waveform data analysis and to construct biophysical computational models. This review focuses on current knowledge regarding the anesthetic mechanism of propofol, an intravenous general anesthetic, by constructing biophysical computational models.
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spelling pubmed-91299302022-05-25 Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review Li, Zhen Liu, Jia Liang, Huazheng Comput Intell Neurosci Review Article The physiological and neuroregulatory mechanism of propofol is largely based on very limited knowledge. It is one of the important puzzling issues in anesthesiology and is of great value in both scientific and clinical fields. It is acknowledged that neural networks which are comprised of a number of neural circuits might be involved in the anesthetic mechanism. However, the mechanism of this hypothesis needs to be further elucidated. With the progress of artificial intelligence, it is more likely to solve this problem through using artificial neural networks to perform temporal waveform data analysis and to construct biophysical computational models. This review focuses on current knowledge regarding the anesthetic mechanism of propofol, an intravenous general anesthetic, by constructing biophysical computational models. Hindawi 2022-05-17 /pmc/articles/PMC9129930/ /pubmed/35619772 http://dx.doi.org/10.1155/2022/8202869 Text en Copyright © 2022 Zhen Li et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Li, Zhen
Liu, Jia
Liang, Huazheng
Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review
title Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review
title_full Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review
title_fullStr Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review
title_full_unstemmed Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review
title_short Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review
title_sort biophysical model: a promising method in the study of the mechanism of propofol: a narrative review
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9129930/
https://www.ncbi.nlm.nih.gov/pubmed/35619772
http://dx.doi.org/10.1155/2022/8202869
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