ArticleScientific reports2022
Revealing low-temperature plasma efficacy through a dose-rate assessment by DNA damage detection combined with machine learning models.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Exploring pH Dynamics in Amino Acid Solutions Under Low-Temperature Plasma Exposure.Molecules (Basel, Switzerland) · 2024Article
- HormoNet: a deep learning approach for hormone-drug interaction prediction.BMC bioinformatics · 2024Article
- A neural network-based PDE solving algorithm with high precision.Scientific reports · 2023Article
- DNA Strand Breaks and Denaturation as Probes of Chemical Reactivity versus Thermal Effects of Atmospheric Pressure Plasma Jets.ACS omega · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
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