A porous magnetic hollow silica nanosphere (MHSN) is a new nanostructured drug carrier for increasing drug loading capability. Keeping the magnetic nanoparticles in the hollow core will limit the toxicity and degradation in a biosystem. In this paper, we report a synthesis of porous MHSNs by sol−gel method. CaCO3/Fe3O4 composite particles were first fabricated by embedding Fe3O4 nanoparticles into CaCO3 using the rotating packed bed (RPB) method. Tetraethoxysilane (TEOS) was then added as precursor to form a silica (SiO2) layer on the surface of CaCO3/Fe3O4 composite particles. Hexadecyltrimethylammonium bromide (CTAB) and octane act as second templates for the formation of porous silica shells. After removing the surfactants by calcination and etching away the CaCO3 particles, porous MHSNs with magnetite (Fe3O4) nanoparticles inside the cores were formed. The pore size can be tuned by adjusting the amount of the cationic surfactant absorbed on the surface of the composite particles to form self-assembled nanochannels. Ibuprofen was loaded on or into the porous MHSNs, and the drug encapsulation and release were investigated. A slow release was observed for the porous MHSNs, which demonstrated MHSNs are potential carriers for controlled releasing in nanomedicine application.

Poly(styrenesulfonic acid)-functionalized materials based on poly(styrenesulfonic acid sodium salt) incorporated via aqueous atom transfer radical polymerization (ATRP) initiated from the surface of large-pore mesoporous SBA-15 silica support have been synthesized. The inorganic-organic nature of these hybrid materials makes them particularly desirable for acid-catalyzed reactions that require extended and hydrophobic surface areas with a narrow pore diameter distribution in the mesoporous range. Acidic hybrid materials were prepared by grafting the ATRP-initiator (3-(chlorodimethylsilyl)propyl bromoisobutyrate) on the silica surface, subsequent polymerization of the styrenesulfonic acid sodium salt monomer, and final sodium ion exchange by acid activation. Conventional and ultra-large-pore SBA-15 silica supports with nominal (BJH) pore diameter ranging from 8 to 32 nm were used for the incorporation of different polymer loadings at different polymerization times. The silylation of ATRP-initiator-functionalized SBA-15 supports has allowed a better control of the ATRP within the mesoporous structure. The use of ultra-large-pore SBA-15 supports provides a remarkable increase of the porosity which allowed us to properly allocate the polymer. The hybrid poly(styrenesulfonic acid)-modified materials showed good catalytic activities in the esterification of oleic acid with n-butanol, particularly in terms of intrinsic activity per acid site.

Purpose Specific absorption rate (SAR) amplification around active implantable medical devices during diagnostic MRI procedures poses a potential risk for patient safety. In this work we present a parallel transmit (pTx) strategy that can be used to safely scan patients with deep brain stimulation (DBS) implants. Methods We performed EM simulations at 3 T using a uniform phantom and a multi-tissue realistic head model with a generic DBS implant. Our strategy is based on utilizing implant-friendly modes which are defined as the modes of an array that reduce the local SAR around the DBS lead tip. These modes are used in a spokes pulse design algorithm in order to produce highly uniform magnitude least-squares flip angle excitations. Results Local SAR (1g) at the lead tip is reduced below 0.1 W/kg in comparison to 31.2W/kg which is obtained by a simple quadrature birdcage excitation without any sort of SAR mitigation. For the multi-tissue realistic head model, peak 10g local SAR and global SAR are obtained as 4.52 W/kg and 0.48 W/kg respectively. A uniform axial flip angle is obtained (NRMSE<3%). Conclusion pTx arrays can be used to generate implant-friendly modes and to reduce SAR around DBS implants while constraining peak local SAR and global SAR and maximizing flip angle homogeneity.

Quantitative Susceptibility Mapping (QSM) estimates the underlying tissue magnetic susceptibility from the gradient echo (GRE) phase signal through background phase removal and dipole inversion steps. Each of these steps typically requires solving an ill-posed inverse problem and thus necessitates additional regularization. Recently developed single-step QSM algorithms directly relate the unprocessed GRE phase to the unknown susceptibility distribution, thereby requiring the solution of a single inverse problem. In this work, we show that such a holistic approach provides susceptibility estimation with artifact mitigation and develop efficient algorithms that involve simple analytical solutions for all of the optimization steps. Our methods employ Total Variation (TV) and Total Generalized Variation (TGV) to jointly perform the background removal and dipole inversion in a single step. Using multiple spherical mean value (SMV) kernels of varying radii permits high fidelity background removal while retaining the phase information in the cortex. Using numerical simulations, we demonstrate that the proposed single-step methods reduce the reconstruction error by up to 66% relative to the multi-step methods that involve SMV background filtering with the same number of SMV kernels, followed by TV- or TGV-regularized dipole inversion. In vivo single-step experiments demonstrate a dramatic reduction in dipole streaking artifacts and improved homogeneity of image contrast. These acquisitions employ the rapid 3D-EPI and the Wave-CAIPI trajectories for SNR-efficient whole-brain imaging. Herein, we also demonstrate the Multi-Echo capability of Wave-CAIPI sequence for the first time, and introduce an automated, phase-sensitive coil sensitivity estimation scheme based on a 4-second calibration acquisition.

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Visual illusions teach us that what we see is not always what it is represented in the physical world. Its special nature make them a fascinating tool to test and validate any new vision model proposed. In general, current vision models are based on the concatenation of linear convolutions and non-linear operations. In this paper we get inspiration from the similarity of this structure with the operations present in Convolutional Neural Networks (CNNs). This motivated us to study if CNNs trained for low-level visual tasks are deceived by visual illusions. In particular, we show that CNNs trained for image denoising, image deblurring, and computational color constancy are able to replicate the human response to visual illusions, and that the extent of this replication varies with respect to variation in architecture and spatial pattern size. We believe that this CNNs behaviour appears as a by-product of the training for the low level vision tasks of denoising, color constancy or deblurring. Our work opens a new bridge between human perception and CNNs: in order to obtain CNNs that better replicate human behaviour, we may need to start aiming for them to better replicate visual illusions.

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