Irrigation of peritoneal cavity with cold atmospheric plasma treated solution effectively reduces microbial load in rat acute peritonitis model

Öztan, Mustafa Onur; Ercan, Utku Kürşat; Gökmen, Ayşegül Aksoy; Şimşek, Fatma; Özdemir, Gazi; Köylüoğlu, Gökhan

doi:10.1038/s41598-022-07598-2

Cited by 13 publications

(13 citation statements)

References 85 publications

(93 reference statements)

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“…CAP applications can be summarized in three main categories; surface modifications, therapeutic applications, and biological decontamination [2]. Antimicrobial activity, blood clotting, tooth whitening, wound healing, and anticancer efficacy are the main biomedical applications of CAP that are reported in the literature [3]. Due to its strong antimicrobial activity against a wide spectrum of microorganisms, including antibiotic-resistant organisms, CAP is an emerging technology that is undergoing intensive research.…”

Section: Introductionmentioning

confidence: 99%

Machine learning to predict the antimicrobial activity of cold atmospheric plasma-activated liquids

Özdemir

Gül

et al. 2023

Mach. Learn.: Sci. Technol.

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Plasma is defined as the fourth state of matter, and non-thermal plasma can be produced at atmospheric pressure under a high electrical field. The strong and broad-spectrum antimicrobial effect of plasma-activated liquids (PALs) is now well known. The antimicrobial effects of PALs depend on many different variables, which complicates the comparison of different studies and determining the most dominant parameters for the antimicrobial effect. The proven applicability of machine learning (ML) in the medical field is encouraging for its application in the field of plasma medicine as well. Thus, ML applications on PALs could present a new perspective to better understand the influences of various parameters on their antimicrobial effects. In this paper, comparative supervised ML models are presented by using previously obtained data to predict the in vitro antimicrobial activity of PALs. A comprehensive literature search was performed, and 12 distinct features related to PAL-microorganism interactions were collected from 33 relevant articles to automatically predict the antimicrobial activity of PALs. After the required normalization, feature encoding, and resampling steps, two supervised ML methods, namely classification and regression, are applied to the data to obtain microbial inactivation (MI) predictions. For classification, MI is labeled in four categories, and for regression, MI is used as a continuous variable. 16 different classifiers and 14 regressors are implemented to predict the MI value. Two different robust cross-validation strategies are conducted for classification and regression models to evaluate the proposed method: repeated stratified k-fold cross-validation and k-fold cross-validation, respectively. We also investigate the effect of different features on models. The results demonstrated that the hyperparameter-optimized Random Forest Classifier (oRFC) and Random Forest Regressor (oRFR) provided superior performance compared to other models for classification and regression. Finally, the best test accuracy of 82.68% for oRFC and R2 of 0.75 for the oRFR are obtained. Furthermore, the determined most important features of predictive models are in line with the outcomes of PALs reported in the literature. An ML framework can accurately predict the antimicrobial activity of PALs without the need for any experimental studies. To the best of our knowledge, this is the first study that investigates the antimicrobial efficacy of PALs with ML. Furthermore, ML techniques could contribute to a better understanding of plasma parameters that have a dominant role in the desired antimicrobial effect. Moreover, such findings may contribute to the definition of a plasma dose in the future.

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Section: Introductionmentioning

confidence: 99%

Machine learning to predict the antimicrobial activity of cold atmospheric plasma-activated liquids

Özdemir

Gül

et al. 2023

Mach. Learn.: Sci. Technol.

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“…It is formed when a gas mixture is subjected to a high voltage, which leads to the formation of charged species, free electrons, ions, and neutrals, in the gas phase, which can then also be found in the liquid phase as well. [ 2 ] Reactive oxygen and nitrogen species (RONS), such as hydrogen peroxide, ozone, and nitric oxide, were produced during the discharge and have been established to have a wide range of biological effects, which leads researchers to apply CAP for different biomedical applications (Figure 1). [ 3 ] CAP exhibits therapeutic effects in various areas including wound healing, cancer therapy, antimicrobial research, dental applications, and dermatology.…”

Section: Introductionmentioning

confidence: 99%

Plasma medicine: The era of artificial intelligence

Ercan

Özdemir

et al. 2023

Plasma Processes & Polymers

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The current trends that incorporate artificial intelligence (AI) and medicine have created new opportunities for improvement in both early diagnosis and treatment of diseases. In this framework, AI might also have the potential to significantly revolutionize the way we approach the field of plasma medicine, an area that is quickly growing and uses cold atmospheric plasma (CAP) to address a variety of medical conditions. Plasma medicine offers promising therapeutic alternatives for conditions widely ranging from cancer treatment to wound healing and antimicrobial applications, but the complexity of the plasma sources and the huge number of parameters may be overwhelming for the determination of underlying mechanisms and for understanding the effect of the source. This is where AI steps in, to provide strong tools for modeling, evaluating, and controlling CAPs. By harnessing the power of AI, researchers in the plasma medicine area, are now able to evaluate massive volumes of data, enhance their treatment protocols, and predict treatment results with a level of precision never possible before. Hereby, we emphasized the potential and further utilization of AI in plasma medicine in light of the fascinating and recent developments in this cooperation. New opportunities are encouraging, but potential limitations, ethical issues, model transparency, and generalizability should be considered. Regardless, the possibilities are endless, and the future of plasma medicine is looking brighter than ever with the implementation of AI.

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Section: Introductionmentioning

confidence: 99%

Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids

Özdemir¹,

Özdemir²,

Gül³

et al. 2022

Preprint

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Plasma is defined as the fourth state of matter and non-thermal plasma can be produced at atmospheric pressure under a high electrical field. The strong and broadspectrum antimicrobial effect of plasma-activated liquids (PALs) is now well known. The antimicrobial effects of PALs depend on many different variables, which complicates the comparison of different studies and determines the most dominant parameters of the antimicrobial effect. The proven applicability of machine learning (ML) in the medical field is encouraging for its application in the field of plasma medicine as well. Thus, ML applications on PALs could present a new perspective to better understand the influences of various parameters on their antimicrobial effects. In this paper, comparative supervised ML models are presented by using previously obtained data to qualitatively predict the in vitro antimicrobial activity of PALs. A literature search was performed and data is collected from 33 relevant articles. After the required normalization, feature encoding, and resampling steps, two supervised ML methods, namely classification, and regression are applied to data to obtain microbial inactivation (MI) predictions. For classification, MI is labeled in four categories and for regression, MI is used as a continuous variable. 16 different classifiers and 14 different regressors are implemented to predict MI value. Two different robust cross-validation strategies are conducted for classification and regression models to evaluate the proposed method; repeated stratified k-fold cross-validation and k-fold cross-validation, respectively. We also investigate the effect of different features on models. The results demonstrated that the hyperparameter-optimized Random Forest Classifier (oRFC) and Random Forest Regressor (oRFR) provided better results than other models for the classification and regression, respectively. Finally, the best test accuracy of 82.68% for oRFC and R 2 of 0.75 for the oRFR are obtained. ML techniques could contribute to a better understanding of plasma parameters that have a dominant * Corresponding author.

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Irrigation of peritoneal cavity with cold atmospheric plasma treated solution effectively reduces microbial load in rat acute peritonitis model

Cited by 13 publications

References 85 publications

Machine learning to predict the antimicrobial activity of cold atmospheric plasma-activated liquids

Machine learning to predict the antimicrobial activity of cold atmospheric plasma-activated liquids

Plasma medicine: The era of artificial intelligence

Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids

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