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Keywords = synomag-D

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15 pages, 3802 KiB  
Article
Magnetic Particle Imaging-Guided Thermal Simulations for Magnetic Particle Hyperthermia
by Hayden Carlton, Nageshwar Arepally, Sean Healy, Anirudh Sharma, Sarah Ptashnik, Maureen Schickel, Matt Newgren, Patrick Goodwill, Anilchandra Attaluri and Robert Ivkov
Nanomaterials 2024, 14(12), 1059; https://doi.org/10.3390/nano14121059 - 20 Jun 2024
Cited by 5 | Viewed by 3202
Abstract
Magnetic particle hyperthermia (MPH) enables the direct heating of solid tumors with alternating magnetic fields (AMFs). One challenge with MPH is the unknown particle distribution in tissue after injection. Magnetic particle imaging (MPI) can measure the nanoparticle content and distribution in tissue after [...] Read more.
Magnetic particle hyperthermia (MPH) enables the direct heating of solid tumors with alternating magnetic fields (AMFs). One challenge with MPH is the unknown particle distribution in tissue after injection. Magnetic particle imaging (MPI) can measure the nanoparticle content and distribution in tissue after delivery. The objective of this study was to develop a clinically translatable protocol that incorporates MPI data into finite element calculations for simulating tissue temperatures during MPH. To verify the protocol, we conducted MPH experiments in tumor-bearing mouse cadavers. Five 8–10-week-old female BALB/c mice bearing subcutaneous 4T1 tumors were anesthetized and received intratumor injections of Synomag®-S90 nanoparticles. Immediately following injection, the mice were euthanized and imaged, and the tumors were heated with an AMF. We used the Mimics Innovation Suite to create a 3D mesh of the tumor from micro-computerized tomography data and spatial index MPI to generate a scaled heating function for the heat transfer calculations. The processed imaging data were incorporated into a finite element solver, COMSOL Multiphysics®. The upper and lower bounds of the simulated tumor temperatures for all five cadavers demonstrated agreement with the experimental temperature measurements, thus verifying the protocol. These results demonstrate the utility of MPI to guide predictive thermal calculations for MPH treatment planning. Full article
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17 pages, 2707 KiB  
Article
Dual Magnetic Particle Imaging and Akaluc Bioluminescence Imaging for Tracking Cancer Cell Metastasis
by Ryan J. Williams, Olivia C. Sehl, Julia J. Gevaert, Shirley Liu, John J. Kelly, Paula J. Foster and John A. Ronald
Tomography 2023, 9(1), 178-194; https://doi.org/10.3390/tomography9010016 - 25 Jan 2023
Cited by 10 | Viewed by 5768
Abstract
Magnetic particle imaging (MPI) provides hotspot tracking and direct quantification of superparamagnetic iron oxide nanoparticle (SPIO)-labelled cells. Bioluminescence imaging (BLI) with the luciferase reporter gene Akaluc can provide complementary information on cell viability. Thus, we explored combining these technologies to provide a more [...] Read more.
Magnetic particle imaging (MPI) provides hotspot tracking and direct quantification of superparamagnetic iron oxide nanoparticle (SPIO)-labelled cells. Bioluminescence imaging (BLI) with the luciferase reporter gene Akaluc can provide complementary information on cell viability. Thus, we explored combining these technologies to provide a more holistic view of cancer cell fate in mice. Akaluc-expressing 4T1Br5 cells were labelled with the SPIO Synomag-D and injected into the mammary fat pads (MFP) of four nude mice. BLI was performed on days 0, 6 and 13, and MPI was performed on days 1, 8 and 14. Ex vivo histology and fluorescence microscopy of MFP and a potential metastatic site was conducted. The BLI signal in the MFP increased significantly from day 0 to day 13 (p < 0.05), mirroring tumor growth. The MPI signal significantly decreased from day 1 to day 14 (p < 0.05) due to SPIO dilution in proliferating cells. Both modalities detected secondary metastases; however, they were visualized in different anatomical regions. Akaluc BLI complemented MPI cell tracking, allowing for longitudinal measures of cell viability and sensitive detection of distant metastases at different locations. We predict this multimodal imaging approach will help to evaluate novel therapeutics and give a better understanding of metastatic mechanisms. Full article
(This article belongs to the Section Cancer Imaging)
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11 pages, 1289 KiB  
Article
Modelling of Dynamic Behaviour in Magnetic Nanoparticles
by Max Tigo Rietberg, Sebastiaan Waanders, Melissa Mathilde Horstman-van de Loosdrecht, Rogier R. Wildeboer, Bennie ten Haken and Lejla Alic
Nanomaterials 2021, 11(12), 3396; https://doi.org/10.3390/nano11123396 - 15 Dec 2021
Cited by 12 | Viewed by 3715
Abstract
The efficient development and utilisation of magnetic nanoparticles (MNPs) for applications in enhanced biosensing relies on the use of magnetisation dynamics, which are primarily governed by the time-dependent motion of the magnetisation due to externally applied magnetic fields. An accurate description of the [...] Read more.
The efficient development and utilisation of magnetic nanoparticles (MNPs) for applications in enhanced biosensing relies on the use of magnetisation dynamics, which are primarily governed by the time-dependent motion of the magnetisation due to externally applied magnetic fields. An accurate description of the physics involved is complex and not yet fully understood, especially in the frequency range where Néel and Brownian relaxation processes compete. However, even though it is well known that non-zero, non-static local fields significantly influence these magnetisation dynamics, the modelling of magnetic dynamics for MNPs often uses zero-field dynamics or a static Langevin approach. In this paper, we developed an approximation to model and evaluate its performance for MNPs exposed to a magnetic field with varying amplitude and frequency. This model was initially developed to predict superparamagnetic nanoparticle behaviour in differential magnetometry applications but it can also be applied to similar techniques such as magnetic particle imaging and frequency mixing. Our model was based upon the Fokker–Planck equations for the two relaxation mechanisms. The equations were solved through numerical approximation and they were then combined, while taking into account the particle size distribution and the respective anisotropy distribution. Our model was evaluated for Synomag®-D70, Synomag®-D50 and SHP-15, which resulted in an overall good agreement between measurement and simulation. Full article
(This article belongs to the Special Issue Biomedical Applications of Anisotropic Magnetic Nanoparticles)
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