使用等效总氧化电位(ETOP)确定血浆剂量:通过机器学习从概念到实际应用

IF 3.5 2区 物理与天体物理 Q2 PHYSICS, APPLIED
E. Wu, K. Song, X. Pei, L. Nie, D. Liu, X. Lu
{"title":"使用等效总氧化电位(ETOP)确定血浆剂量:通过机器学习从概念到实际应用","authors":"E. Wu, K. Song, X. Pei, L. Nie, D. Liu, X. Lu","doi":"10.1063/5.0228789","DOIUrl":null,"url":null,"abstract":"Atmospheric pressure nonequilibrium plasma holds significant potential in biomedical applications due to its ability to generate reactive species at low temperatures. However, accurately quantifying and controlling plasma dosage remains challenging. Although equivalent total oxidation potential (ETOP) has been proposed for defining dosage, previous methods required measurement of various reactive oxygen and nitrogen species (RONS) densities, which are impractical in diverse plasma settings. Efficient ETOP prediction across variable conditions is thus essential. To address this, we propose a machine learning-based ETOP modeling method. This study collected RONS density data under various conditions using laser-induced fluorescence and trained an artificial neural network to predict ETOP values based on input parameters like voltage, gas flow rate, oxygen concentration, and humidity. This approach enables efficient ETOP prediction across variable conditions, supporting the standardization and clinical application of plasma medicine.","PeriodicalId":8094,"journal":{"name":"Applied Physics Letters","volume":"11 1","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Determining plasma dose using equivalent total oxidation potential (ETOP): Concept to practical application via machine learning\",\"authors\":\"E. Wu, K. Song, X. Pei, L. Nie, D. Liu, X. Lu\",\"doi\":\"10.1063/5.0228789\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Atmospheric pressure nonequilibrium plasma holds significant potential in biomedical applications due to its ability to generate reactive species at low temperatures. However, accurately quantifying and controlling plasma dosage remains challenging. Although equivalent total oxidation potential (ETOP) has been proposed for defining dosage, previous methods required measurement of various reactive oxygen and nitrogen species (RONS) densities, which are impractical in diverse plasma settings. Efficient ETOP prediction across variable conditions is thus essential. To address this, we propose a machine learning-based ETOP modeling method. This study collected RONS density data under various conditions using laser-induced fluorescence and trained an artificial neural network to predict ETOP values based on input parameters like voltage, gas flow rate, oxygen concentration, and humidity. This approach enables efficient ETOP prediction across variable conditions, supporting the standardization and clinical application of plasma medicine.\",\"PeriodicalId\":8094,\"journal\":{\"name\":\"Applied Physics Letters\",\"volume\":\"11 1\",\"pages\":\"\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Physics Letters\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://doi.org/10.1063/5.0228789\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PHYSICS, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Physics Letters","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1063/5.0228789","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, APPLIED","Score":null,"Total":0}
引用次数: 0

摘要

常压非平衡等离子体能够在低温下产生活性物质,因此在生物医学应用方面具有巨大潜力。然而,准确量化和控制等离子体的剂量仍然具有挑战性。虽然等效总氧化电位(ETOP)已被提出用于确定剂量,但以前的方法需要测量各种活性氧和氮物种(RONS)的密度,这在不同的等离子体环境中是不切实际的。因此,在不同条件下高效预测 ETOP 至关重要。为此,我们提出了一种基于机器学习的 ETOP 建模方法。这项研究利用激光诱导荧光收集了各种条件下的 RONS 密度数据,并根据电压、气体流速、氧气浓度和湿度等输入参数训练了一个人工神经网络来预测 ETOP 值。这种方法能在不同条件下有效预测 ETOP 值,为等离子体医学的标准化和临床应用提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determining plasma dose using equivalent total oxidation potential (ETOP): Concept to practical application via machine learning
Atmospheric pressure nonequilibrium plasma holds significant potential in biomedical applications due to its ability to generate reactive species at low temperatures. However, accurately quantifying and controlling plasma dosage remains challenging. Although equivalent total oxidation potential (ETOP) has been proposed for defining dosage, previous methods required measurement of various reactive oxygen and nitrogen species (RONS) densities, which are impractical in diverse plasma settings. Efficient ETOP prediction across variable conditions is thus essential. To address this, we propose a machine learning-based ETOP modeling method. This study collected RONS density data under various conditions using laser-induced fluorescence and trained an artificial neural network to predict ETOP values based on input parameters like voltage, gas flow rate, oxygen concentration, and humidity. This approach enables efficient ETOP prediction across variable conditions, supporting the standardization and clinical application of plasma medicine.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Applied Physics Letters
Applied Physics Letters 物理-物理:应用
CiteScore
6.40
自引率
10.00%
发文量
1821
审稿时长
1.6 months
期刊介绍: Applied Physics Letters (APL) features concise, up-to-date reports on significant new findings in applied physics. Emphasizing rapid dissemination of key data and new physical insights, APL offers prompt publication of new experimental and theoretical papers reporting applications of physics phenomena to all branches of science, engineering, and modern technology. In addition to regular articles, the journal also publishes invited Fast Track, Perspectives, and in-depth Editorials which report on cutting-edge areas in applied physics. APL Perspectives are forward-looking invited letters which highlight recent developments or discoveries. Emphasis is placed on very recent developments, potentially disruptive technologies, open questions and possible solutions. They also include a mini-roadmap detailing where the community should direct efforts in order for the phenomena to be viable for application and the challenges associated with meeting that performance threshold. Perspectives are characterized by personal viewpoints and opinions of recognized experts in the field. Fast Track articles are invited original research articles that report results that are particularly novel and important or provide a significant advancement in an emerging field. Because of the urgency and scientific importance of the work, the peer review process is accelerated. If, during the review process, it becomes apparent that the paper does not meet the Fast Track criterion, it is returned to a normal track.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信