Abstract: In recent years, cold atmospheric plasma (CAP) has been proposed as a novel method to enhance transdermal drug delivery, while avoiding tissue damage. However, the underlying mechanism for the increasing skin permeability upon CAP treatment is still undefined. We propose a mechanism in which CAP-generated reactive species induce cross-linking of skin lipids, leading to the generation of nanopores, thereby facilitating the permeation of drug molecules. Molecular dynamics simulations support this proposed mechanism. Furthermore, our results indicate that to achieve maximum enhancement of the permeability, the optimal treatment will depend on the exact lipid composition of the skin, as well as on the CAP source used.

Abstract: Promising biomedical uses for nonthermal plasma (NTP) in the fields of regenerative medicine, cancer therapy, and vaccine delivery involve the noninvasive application of uniform nonequilibrium plasma (including dielectric barrier discharge plasma) to living skin. Whereas most investigations have focused on achieving desired therapeutic outcomes, fewer studies have examined the mechanisms and pathways by which epithelial cells respond to NTP exposure. Using a transwell apical-basolateral-chambered system to culture the human keratinocyte HaCaT cell line, in vitro experiments were performed to demonstrate the effects of nanosecond-pulsed dielectric barrier discharge (nsDBD) plasma on polarized epithelial cell viability, monolayer permeability, intracellular oxidative stress, and the release of adenosine triphosphate (ATP). Application of nsDBD plasma at 60 Hz or below had minimal or no effect on HaCaT monolayer viability or permeability. nsDBD plasma exposure did, however, result in frequency-dependent reductions in intracellular glutathione (indicating direct induction of oxidative stress by nsDBD plasma) and increased extracellular ATP concentrations in the ba-solateral (subepithelial) media, which are indicators of cellular stress and an NTP-induced inflammatory response. These studies provide new insights into nsDBD plasma-induced inflammation and local innate immune responses initiated by polarized epithelial tissues.

Abstract: Studies using xenograft mouse models have shown that plasma applied to the skin overlying tumors results in tumor shrinkage. Plasma is considered a nonpenetrating treatment; however, these studies demonstrate plasma effects that occur beyond the postulated depth of physical penetration of plasma components. The present study examines the propagation of plasma effects through a tissue model using three-dimensional, cell-laden extracellular matrices (ECMs). These ECMs are used as barriers against direct plasma penetration. By placing them onto a monolayer of target cancer cells to create an in-vitro analog to in-vivo studies, we distinguished between cellular effects from direct plasma exposure and cellular effects due to cell-to-cell signaling stimulated by plasma. We show that nanosecond-pulsed dielectric barrier discharge plasma treatment applied atop an acellular barrier impedes the externalization of calreticulin (CRT) in the target cells. In contrast, when a barrier is populated with cells, CRT externalization is restored. Thus, we demonstrate that plasma components stimulate signaling among cells embedded in the barrier to transfer plasma effects to the target cells.

Abstract: Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H-abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well-trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements. In this study, since reactive molecular dynamics simulation can currently only describe interactions between a few hundred atoms in a few hundred picoseconds, deep neural networks (DNN) are introduced to enhance the simulation results by predicting more data efficiently. image