Jean‐Paul Fortin

Iron oxide colloidal nanomagnets generate heat when subjected to an alternating magnetic field. Their heating power, governed by the mechanisms of magnetic energy dissipation for single-domain particles (Brown and Néel relaxations), is highly sensitive to the crystal size, the material, and the solvent properties. This study was designed to distinguish between the contributions of Néel and Brownian mechanisms to heat generation. Anionic nanocrystals of maghemite and cobalt ferrite, differing by their magnetic anisotropy, were chemically synthesized and dispersed in an aqueous suspension by electrostatic stabilization. The particles were size-sorted by successive electrostatic phase separation steps. Parameters governing the efficiency of nanomagnets as heat mediators were varied independently; these comprised the particle size (from 5 to 16.5 nm), the solvent viscosity, magnetic anisotropy, and the magnetic field frequency and amplitude. The measured specific loss powers (SLPs) were in quantitative agreement with the results of a predictive model taking into account both Néel and Brown loss processes and the whole particle size distribution. By varying the carrier fluid viscosity, we found that Brownian friction within the carrier fluid was the main contributor to the heating power of cobalt ferrite particles. In contrast, Néel internal rotation of the magnetic moment accounted for most of the loss power of maghemite particles. Specific loss powers were varied by 3 orders of magnitude with increasing maghemite crystal size (from 4 to 1650 W/g at 700 kHz and 24.8 kA/m). This comprehensive parametric study provides the groundwork for the use of anionic colloidal nanocrystals to generate magnetically induced hyperthermia in various media, including complex systems and biological materials.

Maghemite (gamma-Fe2O3) nanocrystals stable at neutral pH and in isotonic aqueous media were synthesized and encapsulated within large unilamellar vesicles of egg phosphatidylcholine (EPC) and distearoyl-SN-glycero-3-phosphoethanolamine-N-[methoxy(poly(ethylene glycol))-2000] (DSPE-PEG(2000), 5 mol %), formed by film hydration coupled with sequential extrusion. The nonentrapped particles were removed by flash gel exclusion chromatography. The magnetic-fluid-loaded liposomes (MFLs) were homogeneous in size (195 +/- 33 hydrodynamic diameters from quasi-elastic light scattering). Iron loading was varied from 35 up to 167 Fe(III)/lipid mol %. Physical and superparamagnetic characteristics of the iron oxide particles were preserved after liposome encapsulation as shown by cryogenic transmission electron microscopy and magnetization curve recording. In biological media, MFLs were highly stable and avoided ferrofluid flocculation while being nontoxic toward the J774 macrophage cell line. Moreover, steric stabilization ensured by PEG-surface-grafting significantly reduced liposome association with the macrophages. The ratios of the transversal (r2) and longitudinal (r1) magnetic resonance (MR) relaxivities of water protons in MFL dispersions (6 < r2/r1 < 18) ranked them among the best T2 contrast agents, the higher iron loading the better the T2 contrast enhancement. Magnetophoresis demonstrated the possible guidance of MFLs by applying a magnetic field gradient. Mouse MR imaging assessed MFLs efficiency as contrast agents in vivo: MR angiography performed 24 h after intravenous injection of the contrast agent provided the first direct evidence of the stealthiness of PEG-ylated magnetic-fluid-loaded liposomes.

The World Health Organization and other institutions are considering Artificial Intelligence (AI) as a technology that can potentially address some health system gaps, especially the reduction of global health inequalities in low- and middle-income countries (LMICs). However, because most AI-based health applications are developed and implemented in high-income countries, their use in LMICs contexts is recent and there is a lack of robust local evaluations to guide decision-making in low-resource settings. After discussing the potential benefits as well as the risks and challenges raised by AI-based health care, we propose five building blocks to guide the development and implementation of more responsible, sustainable, and inclusive AI health care technologies in LMICs.