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.

Surgical site infections have a remarkable impact on morbidity, extended hospitalization and mortality. Sutures strongly contribute to development of surgical site infections as they are considered foreign material in the human body. Sutures serve as excellent surfaces for microbial adherence and subsequent colonization, biofilm formation and infection on the site of a surgery. Various antimicrobial sutures have been developed to prevent suture-mediated surgical site infection. However, depending on the site of surgery, antimicrobial sutures may remain ineffective, and antimicrobial agents on them might have drawbacks. Plasma, defined as the fourth state of matter, composed of ionized gas, reactive oxygen and nitrogen species, free radical and neutrals, draws attention for the control and prevention of hospital-acquired infections due to its excellent antimicrobial activities. In the present study, the efficacy of non-thermal atmospheric plasma treatment for prevention of surgical site infections was investigated. First, contaminated poly (glycolic-co-lactic acid), polyglycolic acid, polydioxanone and poly (glycolic acid-co-caprolactone) sutures were treated with non-thermal atmospheric plasma to eradicate contaminating bacteria like Staphylococcus aureus and Escherichia coli. Moreover, sutures were pre-treated with non-thermal atmospheric plasma and then exposed to S. aureus and E. coli. Our results revealed that non-thermal atmospheric plasma treatment effectively eradicates contaminating bacteria on sutures, and non-thermal atmospheric plasma pre-treatment effectively prevents bacterial colonization on sutures without altering their mechanical properties. Chemical characterization of sutures was performed with FT-IR and XPS and results showed that non-thermal atmospheric plasma treatment substantially increased the hydrophilicity of sutures which might be the primary mechanism for the prevention of bacterial colonization. In conclusion, plasma-treated sutures could be considered as novel alternative materials for the control and prevention of surgical site infections.

Background: Infectious diseases not only cause severe health problems but also burden the healthcare system. Therefore, the effective treatment of those diseases is crucial. Both conventional approaches, such as antimicrobial agents, and novel approaches, like antimicrobial peptides (AMPs), are used to treat infections. However, due to the drawbacks of current approaches, new solutions are still being investigated. One recent approach is the use of AMPs and antimicrobial agents in combination, but determining synergism is with a huge variety of AMPs time-consuming and requires multiple experimental studies. Machine learning (ML) algorithms are widely used to predict biological outcomes, particularly in the field of AMPs, but no previous research reported on predicting the synergistic effects of AMPs and antimicrobial agents. Results: Several supervised ML models were implemented to accurately predict the synergistic effect of AMPs and antimicrobial agents. The results demonstrated that the hyperparameter-optimized Light Gradient Boosted Machine Classifier (oLGBMC) yielded the best test accuracy of 76.92% for predicting the synergistic effect. Besides, the feature importance analysis reveals that the target microbial species, the minimum inhibitory concentrations (MICs) of the AMP and the antimicrobial agents, and the used antimicrobial agent were the most important features for the prediction of synergistic effect, which aligns with recent experimental studies in the literature. Conclusion: This study reveals that ML algorithms can predict the synergistic activity of two different antimicrobial agents without the need for complex and time-consuming experimental procedures. The implications support that the ML models may not only reduce the experimental cost but also provide validation of experimental procedures.