Heat induction of iron oxide nanoparticles with rational artificial neural network design-based particle swarm optimization for magnetic cancer hyperthermia

Magnetically recoverable Fe3O4@SiO2@SBA-3@2-ATP-Cu: an improved catalyst for the synthesis of 5-substituted 1H-tetrazoles

Functionalization of Fe3O4@SiO2@SBA-3 with double-charged 3-chloropropyltrimethoxysilane (CPTMS) and 2-aminophenol, followed by mechanical mixing of the solid product with copper(i) chloride produces a new, greener and efficient Fe3O4@SiO2@SBA-3@2-ATP-Cu catalyst for the synthesis of 5-substituted 1H-tetrazoles. XRD, SEM, atomic absorption, TGA, N2 adsorption-desorption, and VSM analyses were performed for the characterization of the Fe3O4@SiO2@SBA-3@2-ATP-Cu structure. Nitrogen adsorption-desorption analysis revealed that Fe3O4@SiO2@SBA-3@2-ATP-Cu has a surface area of 242 m2 g-1 and a total pore volume of 55.72 cm3 g-1. In synthesizing 5-substituted 1H-tetrazoles, Fe3O4@SiO2@SBA-3@2-ATP-Cu shows superior yields in short reaction times at 120 °C. This catalyst also showed high thermal stability and recyclability at least for 4 runs without apparent loss of efficiency.

Superparamagnetic Nanocrystals Clustered Using Poly(ethylene glycol)-Crosslinked Amphiphilic Copolymers for the Diagnosis of Liver Cancer

Superparamagnetic iron oxide (SPIO) nanocrystals have been extensively studied as theranostic nanoparticles to increase transverse (T2) relaxivity and enhance contrast in magnetic resonance imaging (MRI). To improve the blood circulation time and enhance the diagnostic sensitivity of MRI contrast agents, we developed an amphiphilic copolymer, PCPZL, to effectively encapsulate SPIO nanocrystals. PCPZL was synthesized by crosslinking a polyethylene glycol (PEG)-based homobifunctional linker with a hydrophobic star-like poly(ε-benzyloxycarbonyl-L-lysine) segment. Consequently, it could self-assemble into shell-crosslinked micelles with enhanced colloidal stability in bloodstream circulation. Notably, PCPZL could effectively load SPIO nanocrystals with a high loading capacity of 66.0 ± 0.9%, forming SPIO nanoclusters with a diameter of approximately 100 nm, a high cluster density, and an impressive T2 relaxivity value 5.5 times higher than that of Resovist®. In vivo MRI measurements highlighted the rapid accumulation and contrast effects of SPIO-loaded PCPZL micelles in the livers of both healthy mice and nude mice with an orthotopic hepatocellular carcinoma tumor model. Moreover, the magnetic micelles remarkably enhanced the relative MRI signal difference between the tumor and normal liver tissues. Overall, our findings demonstrate that PCPZL significantly improves the stability and magnetic properties of SPIO nanocrystals, making SPIO-loaded PCPZL micelles promising MRI contrast agents for diagnosing liver diseases and cancers.

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.

Validation of a Temperature-Feedback Controlled Automated Magnetic Hyperthermia Therapy Device

We present in vivo validation of an automated magnetic hyperthermia therapy (MHT) device that uses real-time temperature input measured at the target to control tissue heating. MHT is a thermal therapy that uses heat generated by magnetic materials exposed to an alternating magnetic field. For temperature monitoring, we integrated a commercial fiber optic temperature probe containing four gallium arsenide (GaAs) temperature sensors. The controller device used temperature from the sensors as input to manage power to the magnetic field applicator. We developed a robust, multi-objective, proportional-integral-derivative (PID) algorithm to control the target thermal dose by modulating power delivered to the magnetic field applicator. The magnetic field applicator was a 20 cm diameter Maxwell-type induction coil powered by a 120 kW induction heating power supply operating at 160 kHz. Finite element (FE) simulations were performed to determine values of the PID gain factors prior to verification and validation trials. Ex vivo verification and validation were conducted in gel phantoms and sectioned bovine liver, respectively. In vivo validation of the controller was achieved in a canine research subject following infusion of magnetic nanoparticles (MNPs) into the brain. In all cases, performance matched controller design criteria, while also achieving a thermal dose measured as cumulative equivalent minutes at 43 °C (CEM43) 60 ± 5 min within 30 min.

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.