Introducing adaptive cold atmospheric plasma: The perspective of adaptive cold plasma cancer treatments based on real-time electrochemical impedance spectroscopy

Lin, Li; Hou, Zichao; Liu, Yi; Sirigiri, J.R.; Lee, Taeyoung; Keidar, Michael

doi:10.1063/5.0003528

Cited by 31 publications

(27 citation statements)

References 38 publications

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“…Note that the in vitro biomedical diagnostics has been introduced in some publications, such as using electric impedance spectroscopy. [ 55 ] Also, the temperature of the target is easy to be measured in real time and passively, [ 25 ] but chemistry is usually the key to plasma therapy. Overall, without a real‐time diagnostic of target situations, especially in vivo, the optimization focusing on the self‐adaptive plasma chemistry might be the only choice to achieve intelligent plasma therapy.…”

Section: Discussionmentioning

confidence: 99%

Self‐Adaptive Plasma Chemistry and Intelligent Plasma Medicine

Lin

Yan

Lee

2021

Advanced Intelligent Systems

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Plasma‐based biomedical applications rely on the reactive oxygen and nitrogen species generated in cold atmospheric plasmas, where complex chemical kinetic schemes occur. The optimization of plasma medicine is thus required for each specific biomedical purpose. In the view of pharmacology, it is to optimize the active pharmaceutical ingredients. This work is thus the first attempt of such a complex task utilizing the recent development of machine learning technologies. Herein, a general method of passive plasma chemical diagnostics and optimization in real time is proposed. Based on spontaneous emission spectroscopy, an artificial neural network provides the gas chemical compositions along with other information such as temperatures. The information further passes through the second neural network which outputs the adjustments of external control inputs including energy, gas injections, and extractions to optimize the plasma chemistry.

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Section: Discussionmentioning

confidence: 99%

Self‐Adaptive Plasma Chemistry and Intelligent Plasma Medicine

Lin

Yan

Lee

2021

Advanced Intelligent Systems

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“…NTP dose in plasma medicine encompasses not only the desired biological effects and clinical outcomes of NTP application, but also the avoidance of undesirable side effects. In the delivery of NTP, dose is influenced by NTP composition and the nature of the target that can alter the characteristics of the plasma produced [ 59 ]. The major source of controversy is the lack of consensus as to the key effectors—physical, chemical, electrical or some combination.…”

Section: Discussionmentioning

confidence: 99%

Differential Effect of Non-Thermal Plasma RONS on Two Human Leukemic Cell Populations

Mohamed

Gebski

Reyes

et al. 2021

Cancers

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Non-thermal plasma application to cancer cells is known to induce oxidative stress, cytotoxicity and indirect immunostimulatory effects on antigen presenting cells (APCs). The purpose of this study was to evaluate the responses of two leukemic cell lines—Jurkat T lymphocytes and THP-1 monocytes—to NTP-generated reactive oxygen and nitrogen species (RONS). Both cell types depleted hydrogen peroxide, but THP-1 cells neutralized it almost immediately. Jurkat cells transiently blunted the frequency-dependent increase in nitrite concentrations in contrast to THP-1 cells, which exhibited no immediate effect. A direct relationship between frequency-dependent cytotoxicity and mitochondrial superoxide was observed only in Jurkat cells. Jurkat cells were very responsive to NTP in their display of calreticulin and heat shock proteins 70 and 90. In contrast, THP-1 cells were minimally responsive or unresponsive. Despite no NTP-dependent decrease in cell surface display of CD47 in either cell line, both cell types induced migration of and phagocytosis by APCs. Our results demonstrate that cells modulate the RONS-mediated changes in liquid chemistry, and, importantly, the resultant immunomodulatory effects of NTP can be independent of NTP-induced cytotoxicity.

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“…Alternatively, when theoretical plasma models are available, surrogate modeling, in which dynamic models are trained based on high-fidelity simulation data [136,137], has proven useful for deriving computationally efficient models suitable for control. Yet, an emerging approach to learning-based MPC is to combine a prior model (data driven or physics-based), which represents our available system knowledge, with a learning-based model that is adapted in real-time [138][139][140]. Such a learning-based modeling scheme is particularly useful for capturing the hard-to-model and timevarying nature of the plasma behavior when it cannot be captured a priori via offline data or high-fidelity simulation data.…”

Section: ML Control Theorymentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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Introducing adaptive cold atmospheric plasma: The perspective of adaptive cold plasma cancer treatments based on real-time electrochemical impedance spectroscopy

Cited by 31 publications

References 38 publications

Self‐Adaptive Plasma Chemistry and Intelligent Plasma Medicine

Self‐Adaptive Plasma Chemistry and Intelligent Plasma Medicine

Differential Effect of Non-Thermal Plasma RONS on Two Human Leukemic Cell Populations

2022 Review of Data-Driven Plasma Science

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