Chitosan hydrogel modified with lanthanum as a drug delivery system for epigallocatechin gallate: Investigation of hydrogel – drug interaction by FT-IR and Raman spectroscopy

Jakubowski, Marcel; Domke, Aleksandra; Ratajczak, Maria; Szczuka, Joanna; Buchwald, Tomasz; Ławniczak, Łukasz; Homa, Jan; Voelkel, Adam; Sandomierski, Mariusz

doi:10.1016/j.saa.2023.122748

Cited by 11 publications

(2 citation statements)

References 48 publications

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“…Lanthanum, a rare earth element, has been used as a doping element or oxide in biomedical applications such as drug delivery, anticancer, and magnetic resonance imaging (MRI) [ 16 , 17 , 18 , 19 ]. In all these applications, there is an improvement in the desired characteristics after lanthanum doping.…”

Section: Introductionmentioning

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

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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.

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Section: Introductionmentioning

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

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“…Surface properties of biomaterials obviously can be analyzed not only by MSI methods, but many others can also serve in this field. A typical example could be given: atomic force microscopy (AFM) [ 1 , 2 ], Raman spectroscopy [ 3 , 4 ], X-ray photoelectron spectroscopy (XPS) [ 5 , 6 ], energy dispersive X-ray spectroscopy (EDX) [ 7 , 8 ], Fourier transform infrared spectroscopy (FT-IR) [ 9 , 10 ] or scanning electron microscopy (SEM) [ 11 , 12 , 13 ]. In fact, several of the above-mentioned methods are simultaneously used to obtain reliable results.…”

Section: Introductionmentioning

confidence: 99%

Mass Spectrometry Imaging of Biomaterials

Kret,

Bodzon-Kulakowska,

Drabik

et al. 2023

Materials

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The science related to biomaterials and tissue engineering accounts for a growing part of our knowledge. Surface modifications of biomaterials, their performance in vitro, and the interaction between them and surrounding tissues are gaining more and more attention. It is because we are interested in finding sophisticated materials that help us to treat or mitigate different disorders. Therefore, efficient methods for surface analysis are needed. Several methods are routinely applied to characterize the physical and chemical properties of the biomaterial surface. Mass Spectrometry Imaging (MSI) techniques are able to measure the information about molecular composition simultaneously from biomaterial and adjacent tissue. That is why it can answer the questions connected with biomaterial characteristics and their biological influence. Moreover, this kind of analysis does not demand any antibodies or dyes that may influence the studied items. It means that we can correlate surface chemistry with a biological response without any modification that could distort the image. In our review, we presented examples of biomaterials analyzed by MSI techniques to indicate the utility of SIMS, MALDI, and DESI—three major ones in the field of biomaterials applications. Examples include biomaterials used to treat vascular system diseases, bone implants with the effects of implanted material on adjacent tissues, nanofibers and membranes monitored by mass spectrometry-related techniques, analyses of drug-eluting long-acting parenteral (LAPs) implants and microspheres where MSI serves as a quality control system.

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Enhanced Bactericidal Effect of Chitosan Using Coordinated Transition Metal Ions

Messias,

de Freitas Marin,

Lazarin-Bidóia

et al. 2023

J Inorg Organomet Polym

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Chitosan hydrogel modified with lanthanum as a drug delivery system for epigallocatechin gallate: Investigation of hydrogel – drug interaction by FT-IR and Raman spectroscopy

Cited by 11 publications

References 48 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

Mass Spectrometry Imaging of Biomaterials

Enhanced Bactericidal Effect of Chitosan Using Coordinated Transition Metal Ions

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