These nanoconstructs are composed of amphiphilic block copolymers with distinct hydrophobic and hydrophilic segments that can self-assemble into supramolecular core±shell structures (usually 10 to 100 nm) in aqueous solution. The hydrophobic micelle core provides an ideal carrier compartment for hydrophobic agents, and the shell consists of a protective corona that stabilizes the nanoparticles. Many hydrophobic drugs such as paclitaxel and doxorubicin have been successfully loaded inside the micelle core to improve drug solubility and pharmacokinetics. [2,3,6,7] In addition to therapeutic applications, polymeric micelles have also received increasing attention in diagnostic imaging applications. When incorporated into micelles, different types of contrast agents have achieved longer blood half-life, improved biocompatibility, and better contrast.[1]In this communication, we report the development of superparamagnetic polymeric micelles as a new class of magnetic resonance imaging (MRI) probes with remarkably high spin± spin (T 2 ) relaxivity and sensitivity. Superparamagnetic iron oxide (SPIO) nanoparticles such as magnetite (FeO´Fe 2 O 3 ) are known to have a strong effect on T 2 . Better detection sensitivity and slower kidney clearance of SPIO nanoparticles make them advantageous over Gd-based small molecular contrast agents. Currently, most T 2 contrast agents are composed of hydrophilic magnetite nanoparticles dispersed in a dextran matrix. [8,9] In contrast, our micelle design consists of a cluster of hydrophobic magnetite particles encapsulated inside the hydrophobic core of polymeric micelle whose surface is stabilized by a poly(ethylene glycol) (PEG) shell. This unique core±shell composite design has allowed us to achieve an ultrasensitive MRI detection limit of 5.2 lg mL ±1 (~5 nM), a sensitivity that promises to expand the ªtool boxº of MR probes for molecular imaging and image-visible drug-delivery applications.We used an amphiphilic diblock copolymer of poly(e-caprolactone)-b-poly(ethylene glycol) (PCL-b-PEG) for the micelle formation (Fig. 1). This copolymer was synthesized by a ringopening polymerization of e-caprolactone using monomethoxy-terminated PEG (5 kDa; 1 Da .

BACKGROUND AND PURPOSE:The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation.

Targeting micelles: Cyclic pentapeptide cRGDfK (red triangles), which targets integrin αvβ3, was conjugated to the outer shell of doxorubicin‐loaded (red hexagons) polymeric micelles by using a post‐micelle modification method. The modified micelles significantly enhanced their internalization (up to 30‐fold) by receptor‐mediated endocytosis in tumor endothelial cells overexpressing the αvβ3 receptor.

A customized deep learning tool is accurate in the detection and quantification of hemorrhage on NCCT. Demonstrated high performance on prospective NCCTs ordered from the emergency department suggests the clinical viability of the proposed deep learning tool.

Beta-lapachone (beta-lap) is a novel anticancer agent that is bioactivated by NADP(H): quinone oxidoreductase 1 (NQO1), an enzyme overexpressed in a variety of tumors. Despite its therapeutic promise, the poor aqueous solubility of beta-lap hinders its preclinical evaluation and clinical translation. Our objective was to develop beta-lap-containing poly(ethylene glycol)-block-poly(D,L-lactide) (PEG-PLA) polymer micelles for the treatment of NQO1-overexpressing tumors. Several micelle fabrication strategies were examined to maximize drug loading. A film sonication method yielded beta-lap micelles with relatively high loading density (4.7+/-1.0% to 6.5+/-1.0%) and optimal size (29.6+/-1.5 nm). Release studies in phosphate-buffered saline (pH 7.4) showed the time (t(1/2)) for 50% of drug release at 18 h. In vitro cytotoxicity assays were performed in NQO1-overexpressing (NQO1+) and NQO1-null (NQO1-) H596 lung, DU-145 prostate, and MDA-MB-231 breast cancer cells. Cytotoxicity data showed that after a 2 h incubation with beta-lap micelles, a marked increase in toxicity was shown in NQO1+ cells over NQO1- cells, resembling free drug both in efficacy and mechanism of cell death. In summary, these data demonstrate the potential of beta-lap micelles as an effective therapeutic strategy against NQO1-overexpressing tumor cells.

No abstract

The purpose of this study was to evaluate the antitumor efficacy and local drug distribution from doxorubicin-containing poly(D,L-lactide-co-glycolide) (PLGA) implants for intratumoral treatment of liver cancer in a rabbit model. Cylindrical polymer millirods (length 8 mm, diameter 1.5 mm) were produced using 65% PLGA, 21.5% NaCl, and 13.5% doxorubicin. These implants were placed in the center of VX2 liver tumors (n = 16, 8 mm in diameter) in rabbits. Tumors were removed 4 and 8 days after millirod implantation, and antitumor efficacy was assessed using tumor size measurements, tumor histology, and fluorescent measurement of drug distribution. The treated tumors were smaller than the untreated controls on both day 4 (0.17 +/- 0.06 vs. 0.31 +/- 0.08 cm(2), p = 0.048) and day 8 (0.14 +/- 0.04 vs. 1.8 +/- 0.8 cm(2), p = 0.025). Drug distribution profiles demonstrated high doxorubicin concentrations (>1000 microg/g) at the tumor core at both time points and drug penetration distances of 2.8 and 1.3 mm on day 4 and 8, respectively. Histological examination confirmed necrosis throughout the tumor tissue. Biodegradable polymer millirods successfully treated the primary tumor mass by providing high doxorubicin concentrations to the tumor tissue over an eight day period.

Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors. Unfortunately, glioma biology is marked by heterogeneous angiogenesis, cellular proliferation, cellular invasion, and apoptosis. This translates into varying degrees of enhancement, edema, and necrosis, making reliable imaging assessment challenging. Deep learning, a subset of machine learning artificial intelligence, has gained traction as a method, which has seen effective employment in solving image-based problems, including those in medical imaging. This review seeks to summarize current deep learning applications used in the field of glioma detection and outcome prediction and will focus on (1) pre- and post-operative tumor segmentation, (2) genetic characterization of tissue, and (3) prognostication. We demonstrate that deep learning methods of segmenting, characterizing, grading, and predicting survival in gliomas are promising opportunities that may enhance both research and clinical activities.