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Background: Silver nanoparticles are extensively researched for their antimicrobial properties. Cold atmospheric plasma, containing reactive oxygen and nitrogen species, is increasingly used for disinfecting microbes, wound healing, and cancer treatment. Therefore, this study examined the effect of water activated by dielectric barrier discharge (DBD) plasma and gliding arc discharge plasma on the antimicrobial activity of silver nanoparticles from Alborzia kermanshahica., Methods: Silver nanoparticles were synthesized using the boiling method, as well as biomass from Alborzia kermanshahica extract grown in water activated by DBD and GA plasma. The physicochemical properties of the synthesized nanoparticles were evaluated using UV-vis spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, dynamic light scattering (DLS), zeta potential analysis, transmission electron microscopy (TEM), and gas chromatography-mass spectrometry (GC-MS) analysis. Additionally, the disk diffusion method was used to assess the antimicrobial efficacy of the manufactured nanoparticles against both Gram-positive and Gram-negative bacteria., Results: The spectroscopy results verified the presence of silver nanoparticles, indicating their biosynthesis. The highest amount of absorption (1.049) belonged to the nanoparticles synthesized by boiling under GA plasma conditions. Comparing the FTIR spectra of the plasma-treated samples with DBD and GA revealed that the DBD-treated samples had more intense peaks, indicating that the DBD method proved to be more effective in enhancing the functional groups on the silver nanoparticles. The DLS results revealed that the boiling method synthesized silver nanoparticles under DBD plasma treatment had a smaller particle size (149.89 nm) with a PDI of 0.251 compared to the GA method, and the DBD method produced nanoparticles with a higher zeta potential (27.7 mV) than the GA method, indicating greater stability of the biosynthesized nanoparticles. Moreover, the highest antimicrobial properties against E. coli (14.333 ± 0.47 mm) were found in the DBD-treated nanoparticles. TEM tests confirmed that spherical nanoparticles attacked the E. coli bacterial membrane, causing cell membrane destruction and cell death. The GC-MS results showed that compounds like 2-methylfuran, 3-methylbutanal, 2-methylbutanal, 3-hydroxy-2-butanone, benzaldehyde, 2-phenylethanol, and 3-octen-2-ol were much higher in the samples that were treated with DBD compared to the samples that were treated with GA plasma., Conclusion: The research indicated that DBD plasma was more efficient than GA plasma in boosting the antimicrobial characteristics of nanoparticles. These results might be a cornerstone for future advancements in utilizing cold plasma to create nanoparticles with enhanced antimicrobial properties., (© 2024. The Author(s).)

Background: Myocardial infarction (MI) is a major cause of death, particularly during the first year. The avoidance of potentially fatal outcomes requires expeditious preventative steps. Machine learning (ML) is a subfield of artificial intelligence science that detects the underlying patterns of available big data for modeling them. This study aimed to establish an ML model with numerous features to predict the fatal complications of MI during the first 72 hours of hospital admission., Methods: We applied an MI complications database that contains the demographic and clinical records of patients during the 3 days of admission based on 2 output classes: dead due to the known complications of MI and alive. We utilized the recursive feature elimination (RFE) method to apply feature selection. Thus, after applying this method, we reduced the number of features to 50. The performance of 4 common ML classifier algorithms, namely logistic regression, support vector machine, random forest, and extreme gradient boosting (XGBoost), was evaluated using 8 classification metrics (sensitivity, specificity, precision, false-positive rate, false-negative rate, accuracy, F1-score, and AUC)., Results: In this study of 1699 patients with confirmed MI, 15.94% experienced fatal complications, and the rest remained alive. The XGBoost model achieved more desirable results based on the accuracy and F1-score metrics and distinguished patients with fatal complications from surviving ones (AUC=78.65%, sensitivity=94.35%, accuracy=91.47%, and F1-score=95.14%). Cardiogenic shock was the most significant feature influencing the prediction of the XGBoost algorithm., Conclusion: XGBoost algorithms can be a promising model for predicting fatal complications following MI., (Copyright© 2023 Tehran University of Medical Sciences. Published by Tehran University of Medical Sciences.)