A comparative study of the action mechanisms and development strategies of different ZnO-based nanostructures in antibacterial and anticancer applications

Ghaffari, Seyed-Behnam; Sarrafzadeh, Mohammad-Hossein; Salami, Maryam; Alvandi, Arvin

doi:10.1016/j.jddst.2023.105221

Cited by 10 publications

(2 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 11 reveals the three most influential features in predicting bacterial survival after removing the treatment duration and the dose of the nanomaterial. This is because the antibacterial properties of ZnO have proved that it is dose-dependent [ 58 ].…”

Section: Resultsmentioning

confidence: 99%

Unraveling the Complex Interactions: Machine Learning Approaches to Predict Bacterial Survival against ZnO and Lanthanum-Doped ZnO Nanoparticles

Navarro-López,

Perfecto-Avalos,

Zavala

et al. 2024

Antibiotics

View full text Add to dashboard Cite

The rise in antibiotic-resistant bacteria is a global health challenge. Due to their unique properties, metal oxide nanoparticles show promise in addressing this issue. However, optimizing these properties requires a deep understanding of complex interactions. This study incorporated data-driven machine learning to predict bacterial survival against lanthanum-doped ZnO nanoparticles. The effect of incorporation of lanthanum ions on ZnO was analyzed. Even with high lanthanum concentration, no significant variations in structural, morphological, and optical properties were observed. The antibacterial activity of La-doped ZnO nanoparticles against Gram-positive and Gram-negative bacteria was qualitatively and quantitatively evaluated. Nanoparticles induce 60%, 95%, and 55% bacterial death against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, respectively. Algorithms such as Multilayer Perceptron, K-Nearest Neighbors, Gradient Boosting, and Extremely Random Trees were used to predict the bacterial survival percentage. Extremely Random Trees performed the best among these models with 95.08% accuracy. A feature relevance analysis extracted the most significant attributes to predict the bacterial survival percentage. Lanthanum content and particle size were irrelevant, despite what can be assumed. This approach offers a promising avenue for developing effective and tailored strategies to reduce the time and cost of developing antimicrobial nanoparticles.

show abstract

Section: Resultsmentioning

confidence: 99%

Unraveling the Complex Interactions: Machine Learning Approaches to Predict Bacterial Survival against ZnO and Lanthanum-Doped ZnO Nanoparticles

Navarro-López,

Perfecto-Avalos,

Zavala

et al. 2024

Antibiotics

View full text Add to dashboard Cite

show abstract

“…8,19–21 More specifically, ZNSs have been extensively investigated in biomedical applications due to their biocompatibility and ease of tailoring their optical, electrical, structural, and morphological properties. 22–24…”

Section: Introductionmentioning

confidence: 99%

ZnO based 0–3D diverse nano-architectures, films and coatings for biomedical applications

Krishna,

Jakmunee,

Mishra

et al. 2024

J. Mater. Chem. B

View full text Add to dashboard Cite

show abstract

ZnO nanomaterials with enhanced antimicrobial activity obtained by Eichhornia crassipes aqueous extract‐mediated synthesis

López González,

Gutiérrez,

García

et al. 2024

J of Chemical Tech & Biotech

View full text Add to dashboard Cite

BackgroundZinc oxide nanoparticles (ZnO NPs) were obtained through precipitation synthesis, using aqueous water hyacinth leaf extract (Eichhornia crassipes) under alkaline conditions. The effect of the extract on the physicochemical and antimicrobial properties of the ZnO was evaluated starting from concentrated extract (labeled as E100) and performing two dilutions of this concentrated extract at 50 and 25% in water (E50, and E25).ResultsThe FTIR spectra revealed, strong peaks around 3440 cm‐1, corresponding to vibrations from O‐H stretching and medium‐intense peaks at 1384, and 1327 cm‐1, assigned to C=C, ‐COOH, and C‐O‐C vibrations, which are related to the presence of the extract; a weak band at 884 cm‐1, and a broad band in the region below 600 cm‐1, indicate the presence of Zn‐O. The hydrodynamic size of the ZnOE100, ZnOE50, and ZnOE25 samples were 202, 244, and 313 nm, respectively. No significant variations on the Eg value were observed. FESEM images showed that the ZnO NPs have triangular‐like shapes, and agglomeration of the nanoparticles synthesized for the ZnOE25 sample. The Minimum Inhibitory Concentration (MIC) of the ZnO NPs showed no significant effects related to the concentration the plant extract used.Conclusionsvariations in particle size and zeta potential can be introduced in the ZnO varying the content of the extract, the presence of organic groups from the extract, along with positive zeta potential value, can promote the bactericide effect of ZnO nanomaterials prepared by bio‐assisted synthesis.This article is protected by copyright. All rights reserved.

show abstract

A comparative study of the action mechanisms and development strategies of different ZnO-based nanostructures in antibacterial and anticancer applications

Cited by 10 publications

References 76 publications

Unraveling the Complex Interactions: Machine Learning Approaches to Predict Bacterial Survival against ZnO and Lanthanum-Doped ZnO Nanoparticles

Unraveling the Complex Interactions: Machine Learning Approaches to Predict Bacterial Survival against ZnO and Lanthanum-Doped ZnO Nanoparticles

ZnO based 0–3D diverse nano-architectures, films and coatings for biomedical applications

ZnO nanomaterials with enhanced antimicrobial activity obtained by Eichhornia crassipes aqueous extract‐mediated synthesis

Contact Info

Product

Resources

About