DNA Strand Breaks and Denaturation as Probes of Chemical Reactivity versus Thermal Effects of Atmospheric Pressure Plasma Jets

Abstract

An atmospheric pressure plasma jet (APPJ) is being advanced as an alternative radiation type that offers excellent efficacy in an array of medical applications against specific biological targets such as DNA. This work explores the possibility of implementing DNA and its damage as a probe for specific plasma diagnostics such as reactive plasma species formation and transient local heating. We analyzed both APPJ characteristics based on the detection of plasma-induced strand breaks and DNA denaturation. Further, we implemented a machine learning model based on artificial neural networks to predict the type and extent of DNA damage for a given combination of APPJ parameter values. This methodology is an important step toward deciphering and explaining the potential adverse effects of APPJ on biological samples of any prospective interest in medicine.

Quantifying hydroxyl radicals generated by a low-temperature plasma using coumarin: methodology and precautions

“…The obtained yields of hydroxyl radicals at different plasma conditions confirm our previous postulations of increase in reactive species formation with higher voltage and frequency as we deduced based on the experiments on DNA damage induced by LTP, for which direct measurement of HO was not possible. 38,39…”

Clinical application of cold atmospheric-pressure plasma: mechanisms and irradiation conditions

“…Histones, around which DNA is wound, undergo various post-translational modifications, including methylation, acetylation, and phosphorylation [98][99][100]. These modifications can influence chromatin structure and, consequently, gene expression.…”

Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and AI Algorithms

“…Building multivariable non-linear prediction models to describe the intricate behavior of CAP interacting with cancer cells are essential for guiding efficient cancer treatment strategies [86]. AI models can thus be used for real-time monitoring and diagnostics [87,[91][92][93], detecting any deviations from the normal behavior [94,95,98], and predicting when maintenance or adjustment is required [85] for maintaining optimal real-time control of the operational parameters. Thus, AI based approaches, specifically ML can significantly improve the reliability and performance of CAP by adapting to dynamic and uncertain environments.…”