Heat induction in two-dimensional graphene-Fe3O4 nanohybrids for magnetic hyperthermia applications with artificial neural network modeling

Nanocomposites Based on Iron Oxide and Carbonaceous Nanoparticles: From Synthesis to Their Biomedical Applications

Nanocomposites based on Fe3O4 and carbonaceous nanoparticles (CNPs), including carbon nanotubes (CNTs) and graphene derivatives (graphene oxide (GO) and reduced graphene oxide (RGO)), such as Fe3O4@GO, Fe3O4@RGO, and Fe3O4@CNT, have demonstrated considerable potential in a number of health applications, including tissue regeneration and innovative cancer treatments such as hyperthermia (HT). This is due to their ability to transport drugs and generate localized heat under the influence of an alternating magnetic field on Fe3O4. Despite the promising potential of CNTs and graphene derivatives as drug delivery systems, their use in biological applications is hindered by challenges related to dispersion in physiological media and particle agglomeration. Hence, a solid foundation has been established for the integration of various synthesis techniques for these nanocomposites, with the wet co-precipitation method being the most prevalent. Moreover, the dimensions and morphology of the composite nanoparticles are directly correlated with the value of magnetic saturation, thus influencing the efficiency of the composite in drug delivery and other significant biomedical applications. The current demand for this type of material is related to the loading of a larger quantity of drugs within the hybrid structure of the carrier, with the objective of releasing this amount into the tumor cells. A second demand refers to the biocompatibility of the drug carrier and its capacity to permeate cell membranes, as well as the processes occurring within the drug carriers. The main objective of this paper is to review the synthesis methods used to prepare hybrids based on Fe3O4 and CNPs, such as GO, RGO, and CNTs, and to examinate their role in the formation of hybrid nanoparticles and the correlation between their morphology, the dimensions, and optical/magnetic properties.

Chitosan-Functionalized Lithium Iron Oxide Nanoparticles for Magnetic Hyperthermia Applications

In this study, various compositions of α-Fe2O3, Li3xFe2-xO3, where x = 0.1, 0.3, and 0.5, along with chitosan (CTS)-coated Li1.5Fe1.5O3 nanomaterials (NMs), were synthesized using a sol-gel method. Rietveld refinement analysis indicated a predominance of the rhombohedral phase for lower Li-doped content (x = 0.1) and a transition to cubic crystal structures at higher Li-doped content (x = 0.3 and 0.5) within the host lattice. Field emission scanning electron microscopy (FE-SEM) images revealed irregular spherical morphologies, while transmission electron microscopy (TEM) images showed average particle sizes ranging from 19 to 40 nm across the various NMs. Superconducting quantum interference device (SQUID) analysis demonstrated a ferromagnetic nature with the highest saturation magnetization measured at 49.84 emu/g for Li1.5Fe1.5O3 NMs. X-ray photoelectron spectra (XPS) exhibited Fe 2p3/2 and Fe 2p1/2 peaks at 712.60 and 726.13 eV, respectively, Li 1s at 57.58 eV, and O 1s at 533.44 eV for the representative samples; these characteristic XPS peaks shifted to a lower binding energy for CTS-coated Li1.5Fe1.5O3 NMs. Hyperthermia studies demonstrated that the Li-doped samples reached a temperature range between 42 and 44 °C under an alternating current (AC) magnetic field applied at 167.6 to 335.2 Oe, with a constant frequency of 278 kHz. The specific absorption rate (SAR) was recorded as 265.11 W/g for Li1.5Fe1.5O3 and 153.48 W/g for CTS-coated Li1.5Fe1.5O3 NMs, both surpassing the SAR values of the other samples. Furthermore, various machine learning techniques were utilized to analyze how different synthesis conditions and material properties affected the heating efficiency and SAR values of the synthesized materials. The study also suggests an optimized set of guidelines and heuristics to enhance the heating performance and SAR values of these materials. Finally, magnetic CTS-coated Li1.5Fe1.5O3 NMs exhibited a higher cell viability, as confirmed by MTT assays conducted on the NRK 52 E normal cell line.

Design of an imaging magnetic microsphere based on photopolymerization for magnetic hyperthermia in tumor therapy

Magnetic hyperthermia therapy has been widely used in the nonsurgical treatment of patients with advanced stage cancers that cannot be treated by surgery. It is minimally invasive, precise, and highly efficient and has a good curative effect. In this paper, a magnetic microsphere with Fe3O4 was prepared for thermal therapy and imaging based on a photoinitiated suspension polymerization method from biallelic monomers. The preparation method clearly minimized the degradative chain transfer of allyl polymerization reactions. The microspheres were characterized by microscope observation, spectral analysis, thermal analysis, and magnetic testing. The magnetothermal effect was detected by an infrared thermal imager in vitro and in vivo under a high-frequency alternating magnetic field (AMF). The antitumor effect was verified by testing the viability of H22 cells and observing a tumor-bearing mouse model under high-frequency AMF. Biocompatibility was evaluated by cell viability assay, tissue section observation, and blood biochemical analysis. The imaging capacity was tested by X-ray, MRI, and CT imaging experiments. The results show that the product has good dispersibility, thermal stability, superparamagnetism, and biocompatibility. Under the action of an AMF, the magnetic hyperthermia effect in tumor-bearing mice was better, and an antitumor effect could be achieved.

A Review of Advanced Multifunctional Magnetic Nanostructures for Cancer Diagnosis and Therapy Integrated into an Artificial Intelligence Approach

The new era of nanomedicine offers significant opportunities for cancer diagnostics and treatment. Magnetic nanoplatforms could be highly effective tools for cancer diagnosis and treatment in the future. Due to their tunable morphologies and superior properties, multifunctional magnetic nanomaterials and their hybrid nanostructures can be designed as specific carriers of drugs, imaging agents, and magnetic theranostics. Multifunctional magnetic nanostructures are promising theranostic agents due to their ability to diagnose and combine therapies. This review provides a comprehensive overview of the development of advanced multifunctional magnetic nanostructures combining magnetic and optical properties, providing photoresponsive magnetic platforms for promising medical applications. Moreover, this review discusses various innovative developments using multifunctional magnetic nanostructures, including drug delivery, cancer treatment, tumor-specific ligands that deliver chemotherapeutics or hormonal agents, magnetic resonance imaging, and tissue engineering. Additionally, artificial intelligence (AI) can be used to optimize material properties in cancer diagnosis and treatment, based on predicted interactions with drugs, cell membranes, vasculature, biological fluid, and the immune system to enhance the effectiveness of therapeutic agents. Furthermore, this review provides an overview of AI approaches used to assess the practical utility of multifunctional magnetic nanostructures for cancer diagnosis and treatment. Finally, the review presents the current knowledge and perspectives on hybrid magnetic systems as cancer treatment tools with AI models.

The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research

The development of nanomedicine involves complex nanomaterial research involving magnetic nanomaterials and their use in magnetic hyperthermia. The selection of the optimal treatment strategies is time-consuming, expensive, unpredictable, and not consistently effective. Delivering personalized therapy that obtains maximal efficiency and minimal side effects is highly important. Thus, Artificial Intelligence (AI) based algorithms provide the opportunity to overcome these crucial issues. In this paper, we briefly overview the significance of the combination of AI-based methods, particularly the Machine Learning (ML) technique, with magnetic hyperthermia. We considered recent publications, reports, protocols, and review papers from Scopus and Web of Science Core Collection databases, considering the PRISMA-S review methodology on applying magnetic nanocarriers in magnetic hyperthermia. An algorithmic performance comparison in terms of their types and accuracy, data availability taking into account their amount, types, and quality was also carried out. Literature shows AI support of these studies from the physicochemical evaluation of nanocarriers, drug development and release, resistance prediction, dosing optimization, the combination of drug selection, pharmacokinetic profile characterization, and outcome prediction to the heat generation estimation. The papers reviewed here clearly illustrate that AI-based solutions can be considered as an effective supporting tool in drug delivery, including optimization and behavior of nanocarriers, both in vitro and in vivo, as well as the delivery process. Moreover, the direction of future research, including the prediction of optimal experiments and data curation initiatives has been indicated.

Influencing Factors of Negative Motivation in College Students’ English Learning Relying on the Artificial Neural Network Algorithm

College English has received increasing focus as an important part of the education system. However, the continuous development of English instruction has not simultaneously promoted students’ positive learning motivation for English courses. The generation and growth of negative motivation have become a common problem among college students. Students’ enthusiasm for learning English courses is gradually fading and teachers’ teaching value has also become difficult to guarantee, which seriously affects the normal and orderly progress of education and teaching activities. Therefore, it is very important for the healthy development of English teaching to understand and study the affecting elements of negative motivation in English learning of university students and to provide scientific and effective suggestions for teachers and learners to establish a good teaching and learning attitude. Relying on the interpretation of a negative motivation theory, this paper studies various influencing factors by means of the artificial neural network algorithm. The principal component analysis method is introduced to improve the traditional BP algorithm in terms of the frequency of iterations and the length of computation time, which realizes the accurate and efficient analysis of college students’ English learning data. The results of the analysis revealed that the comprehensive error of this algorithm in the analysis of influencing factors was in the range of 0.004 to 0.012. Through the calculation of the eigenvalues and cumulative contribution rate of negative motivation influencing factors, it is found that factors such as the curriculum setting, teaching method, and teacher-student relationship have the greatest influence on students’ negative motivation in English learning. The eigenvalues were 1.027, 1.319, and 1.422, respectively. The cumulative contribution rate reached 64.57%, 26.11%, and 23.62%, respectively. From this aspect, it is necessary to improve these aspects in order to eliminate the negative motivation of learning.