Dataset Information

Radiofrequency-Triggered Drug Release from Nanoliposomes with Millimeter-Scale Resolution Using a Superimposed Static Gating Field.

ABSTRACT: Drug delivery to a specific site in the body typically relies on the use of targeting agents that recognize a unique biomarker. Unfortunately, it is often difficult to identify unique molecular signatures that exist only at the site of interest. An alternative strategy is to deliver energy (e.g., light) to locally trigger release from a drug carrier; however, the use of this approach is limited because energy delivery to deep tissues is often impractical or invasive. In this work, radiofrequency-responsive superparamagnetic iron oxide nanoparticles (SPIONs) are used to trigger drug release from nanoscale vesicles. Because the body is inherently nonmagnetic, this approach allows for deep tissue targeting. To overcome the unfavorable meter-scale diffraction limit of SPION-compatible radiofrequency (RF) fields, a strong static gating field containing a sharp zero point is superimposed on the RF field. Only drug carriers that are at or near the zero point are susceptible to RF-triggered drug release, thereby localizing drug delivery with millimeter-scale resolution. This approach induces >40% drug release from thermally responsive doxorubicin-loaded liposomes within a 3.2 mm radius of the zero point with <10% release in the surrounding area, leading to a >2.5 therapeutic index in Huh 7 hepatocellular carcinoma cells.

SUBMITTER: Liu JF

PROVIDER: S-EPMC6397654 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Publications

Radiofrequency-Triggered Drug Release from Nanoliposomes with Millimeter-Scale Resolution Using a Superimposed Static Gating Field.

Liu Jessica F JF Neel Nishant N Dang Phillip P Lamb Max M McKenna Jaime J Rodgers Lauren L Litt Brian B Cheng Zhiliang Z Tsourkas Andrew A Issadore David D

Small (Weinheim an der Bergstrasse, Germany) 20181004 44

Drug delivery to a specific site in the body typically relies on the use of targeting agents that recognize a unique biomarker. Unfortunately, it is often difficult to identify unique molecular signatures that exist only at the site of interest. An alternative strategy is to deliver energy (e.g., light) to locally trigger release from a drug carrier; however, the use of this approach is limited because energy delivery to deep tissues is often impractical or invasive. In this work, radiofrequency ...[more]

PMID: 30286280

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Project description:Automatic cerebral cortical surface reconstruction is a useful tool for cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T1-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-mm isotropic resolution for improved accuracy of cortical surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of cortical surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the cortical surface reconstruction resulting from denoised single-repetition sub-millimeter T1-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ~0.016, high whole-brain averaged peak signal-to-noise ratio of ~33.5 dB and structural similarity index of ~0.92, and minimal gray matter-white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray matter-white matter surface placement, gray matter-cerebrospinal fluid surface placement and cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm-sufficiently accurate for most applications. These discrepancies were approximately one third to half of those from 1-mm isotropic resolution data. The denoising performance was equivalent to averaging ~2.5 repetitions of the data in terms of image similarity, and 1.6-2.2 repetitions in terms of the cortical surface placement accuracy. The scan-rescan variability of the cortical surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved cortical surface reconstruction at sub-millimeter resolution.

Dataset Information

Radiofrequency-Triggered Drug Release from Nanoliposomes with Millimeter-Scale Resolution Using a Superimposed Static Gating Field.

Publications

Radiofrequency-Triggered Drug Release from Nanoliposomes with Millimeter-Scale Resolution Using a Superimposed Static Gating Field.

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