Radiation Laboratory, University of Notre Dame, Notre Dame, IN, 46556, USA.
Department of Physics and Astronomy, University of Notre Dame, Notre Dame, IN, 46556, USA.
Sci Rep. 2022 Nov 1;12(1):18353. doi: 10.1038/s41598-022-21783-3.
Low-temperature plasmas have quickly emerged as alternative and unconventional types of radiation that offer great promise for various clinical modalities. As with other types of radiation, the therapeutic efficacy and safety of low-temperature plasmas are ubiquitous concerns, and assessing their dose rates is crucial in clinical settings. Unfortunately, assessing the dose rates by standard dosimetric techniques has been challenging. To overcome this difficulty, we proposed a dose-rate assessment framework that combined the predictive modeling of plasma-induced damage in DNA by machine learning with existing radiation dose-DNA damage correlations. Our results indicated that low-temperature plasmas have a remarkably high dose rate that can be tuned by various process parameters. This attribute is beneficial for inducing radiobiological effects in a more controllable manner.
低温等离子体作为替代和非常规类型的辐射已经迅速崛起，为各种临床模式带来了巨大的前景。与其他类型的辐射一样，低温等离子体的治疗效果和安全性是普遍关注的问题，评估其剂量率在临床环境中至关重要。不幸的是，使用标准剂量测量技术评估剂量率一直具有挑战性。为了克服这一困难，我们提出了一个剂量率评估框架，该框架将机器学习预测模型与现有的辐射剂量-DNA 损伤相关性相结合，用于评估等离子体诱导 DNA 损伤。我们的结果表明，低温等离子体具有很高的剂量率，并且可以通过各种工艺参数进行调节。这一特性有利于以更可控的方式诱导放射生物学效应。
