Development of CD133 Targeting Multi-Drug Polymer Micellar Nanoparticles for Glioblastoma - In Vitro Evaluation in Glioblastoma Stem Cells.

PURPOSE: Glioblastoma (GBM) is a malignant brain tumor with a poor long-term prognosis due to recurrence from highly resistant GBM cancer stem cells (CSCs), for which the current standard of treatment with temozolomide (TMZ) alone will unlikely produce a viable cure. In addition, CSCs regenerate rapidly and overexpress methyl transferase which overrides the DNA-alkylating mechanism of TMZ, leading to resistance. The objective of this research was to apply the concepts of nanotechnology to develop a multi-drug therapy, TMZ and idasanutlin (RG7388, a potent mouse double minute 2 (MDM2) antagonist), loaded in functionalized nanoparticles (NPs) that target the GBM CSC subpopulation, reduce the cell viability and provide possibility of in vivo preclinical imaging. METHODS: Polymer-micellar NPs composed of poly(styrene-b-ethylene oxide) (PS-b-PEO) and poly(lactic-co-glycolic) acid (PLGA) were developed by a double emulsion technique loading TMZ and/or RG7388. The NPs were covalently bound to a 15-nucleotide base-pair CD133 aptamer to target the CD133 antigen expressed on the surfaces of GBM CSCs. For diagnostic functionality, the NPs were labelled with radiotracer Zirconium-89 (89Zr). RESULTS: NPs maintained size range less than 100 nm, a low negative charge and exhibited the ability to target and kill the CSC subpopulation when TMZ and RG7388 were used in combination. The targeting function of CD133 aptamer promoted killing in GBM CSCs providing impetus for further development of targeted nanosystems for localized therapy in future in vivo models. CONCLUSIONS: This work has provided a potential clinical application for targeting GBM CSCs with simultaneous diagnostic imaging.

Determining scientific impact using a collaboration index.

Researchers collaborate on scientific projects that are often measured by both the quantity and the quality of the resultant peer-reviewed publications. However, not all collaborators contribute to these publications equally, making metrics such as the total number of publications and the H-index insufficient measurements of individual scientific impact. To remedy this, we use an axiomatic approach to assign relative credits to the coauthors of a given paper, referred to as the A-index for its axiomatic foundation. In this paper, we use the A-index to compute the weighted sums of peer-reviewed publications and journal impact factors, denoted as the C- and P-indexes for collaboration and productivity, respectively. We perform an in-depth analysis of bibliometric data for 186 biomedical engineering faculty members and from extensive simulation. It is found that these axiomatically weighted indexes better capture a researcher's scientific caliber than do the total number of publications and the H-index, allowing for fairer and sharper evaluation of researchers with diverse collaborative behaviors.

Dynamic Contrast-enhanced MR Imaging Curve-type Analysis: Is It Helpful in the Differentiation of Prostate Cancer from Healthy Peripheral Zone?

PURPOSE: To evaluate the performance and interobserver agreement of qualitative dynamic contrast material enhanced magnetic resonance (MR) imaging curve analysis as described in the Prostate Imaging Reporting and Data System (PI-RADS) for the differentiation of prostate cancer (PCa) from healthy prostatic tissue in the peripheral zone (PZ). MATERIALS AND METHODS: This Health Insurance Portability and Accountability Act-compliant institutional review board-approved retrospective analysis included 120 consecutive pretreatment dynamic contrast-enhanced (DCE) MR imaging PCa examinations. Regions of interest (ROIs) were placed in 251 spots, including 95 (37.8%) in healthy PZ tissue and 156 (62.2%) in PCa, by using detailed histologic-multiparametric MR correlation review. Three radiologists reviewed the DCE time curves and assessed qualitative curve types as described in PI-RADS: type 1 (progressive), type 2 (plateau), or type 3 (washout). Receiver operating characteristic curve analysis was used to assess accuracy in differentiating PCa from healthy tissue on the basis of curve type, and κ was calculated to assess interobserver agreement. RESULTS: Receiver operating characteristic curves were similar for all observers, but mean areas under the receiver operating characteristic curve were poor (0.58 ± 0.04 [standard deviation] to 0.63 ± 0.04). No differences in accuracy were seen for varying DCE time resolution and imaging length. Observer agreement in assessment of type 3 versus types 1 or 2 curves was substantial (0.66 < κ < 0.79), better for PCa ROIs than for healthy-tissue ROIs. The agreement between type 1 and type 2 curves was moderate to substantial (0.49 < κ < 0.78). CONCLUSION: Qualitative DCE MR imaging time-curve-type analysis performs poorly for differentiation of PCa from healthy prostatic tissue. Interobserver agreement is excellent in assessment of type 3 curves but only moderate for type 1 and 2 curves.

CT colonography with computer-aided detection: recognizing the causes of false-positive reader results.

Computed tomography (CT) colonography is a screening modality used to detect colonic polyps before they progress to colorectal cancer. Computer-aided detection (CAD) is designed to decrease errors of detection by finding and displaying polyp candidates for evaluation by the reader. CT colonography CAD false-positive results are common and have numerous causes. The relative frequency of CAD false-positive results and their effect on reader performance on the basis of a 19-reader, 100-case trial shows that the vast majority of CAD false-positive results were dismissed by readers. Many CAD false-positive results are easily disregarded, including those that result from coarse mucosa, reconstruction, peristalsis, motion, streak artifacts, diverticulum, rectal tubes, and lipomas. CAD false-positive results caused by haustral folds, extracolonic candidates, diminutive lesions (<6 mm), anal papillae, internal hemorrhoids, varices, extrinsic compression, and flexural pseudotumors are almost always recognized and disregarded. The ileocecal valve and tagged stool are common sources of CAD false-positive results associated with reader false-positive results. Nondismissable CAD soft-tissue polyp candidates larger than 6 mm are another common cause of reader false-positive results that may lead to further evaluation with follow-up CT colonography or optical colonoscopy. Strategies for correctly evaluating CAD polyp candidates are important to avoid pitfalls from common sources of CAD false-positive results.

Novel therapeutics and drug-delivery approaches in the modulation of glioblastoma stem cell resistance.

Glioblastoma (GBM) is a deadly malignancy with a poor prognosis. An important factor contributing to GBM recurrence is high resistance of GBM cancer stem cells (GSCs). While temozolomide (TMZ), has been shown to consistently extend survival, GSCs grow resistant to TMZ through upregulation of DNA damage repair mechanisms and avoidance of apoptosis. Since a single-drug approach has failed to significantly alter prognosis in the past 15 years, unique approaches such as multidrug combination therapy together with distinctive targeted drug-delivery approaches against cancer stem cells are needed. In this review, a rationale for multidrug therapy using a targeted nanotechnology approach that preferentially target GSCs is proposed with discussion and examples of drugs, nanomedicine delivery systems, and targeting moieties.

Aerosolized In Vivo 3D Localization of Nose-to-Brain Nanocarrier Delivery Using Multimodality Neuroimaging in a Rat Model-Protocol Development.

The fate of intranasal aerosolized radiolabeled polymeric micellar nanoparticles (LPNPs) was tracked with positron emission tomography/computer tomography (PET/CT) imaging in a rat model to measure nose-to-brain delivery. A quantitative temporal and spatial testing protocol for new radio-nanotheranostic agents was sought in vivo. LPNPs labeled with a zirconium 89 (89Zr) PET tracer were administered via intranasal or intravenous delivery, followed by serial PET/CT imaging. After 2 h of continuous imaging, the animals were sacrificed, and the brain substructures (olfactory bulb, forebrain, and brainstem) were isolated. The activity in each brain region was measured for comparison with the corresponding PET/CT region of interest via activity measurements. Serial imaging of the LPNPs (100 nm PLA-PEG-DSPE+89Zr) delivered intranasally via nasal tubing demonstrated increased activity in the brain after 1 and 2 h following intranasal drug delivery (INDD) compared to intravenous administration, which correlated with ex vivo gamma counting and autoradiography. Although assessment of delivery from nose to brain is a promising approach, the technology has several limitations that require further development. An experimental protocol for aerosolized intranasal delivery is presented herein, which may provide a platform for better targeting the olfactory epithelium.

Imaging of intranasal drug delivery to the brain.

Intranasal (IN) delivery is a rapidly developing area for therapies with great potential for the treatment of central nervous system (CNS) diseases. Moreover, in vivo imaging is becoming an important part of therapy assessment, both clinically in humans and translationally in animals. IN drug delivery is an alternative to systemic administration that uses the direct anatomic pathway between the olfactory/trigeminal neuroepithelium of the nasal mucosa and the brain. Several drugs have already been approved for IN application, while others are undergoing development and testing. To better understand which imaging modalities are being used to assess IN delivery of therapeutics, we performed a literature search with the key words "Intranasal delivery" and "Imaging" and summarized these findings in the current review. While this review does not attempt to be fully comprehensive, we intend for the examples provided to allow a well-rounded picture of the imaging tools available to assess IN delivery, with an emphasis on the nose-to-brain delivery route. Examples of in vivo imaging, for both humans and animals, include magnetic resonance imaging (MRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), gamma scintigraphy and computed tomography (CT). Additionally, some in vivo optical imaging modalities, including bioluminescence and fluorescence, have been used more in experimental testing in animals. In this review, we introduce each imaging modality, how it is being utilized and outline its strengths and weaknesses, specifically in the context of IN delivery of therapeutics to the brain.

CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE).

In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel 1×1 CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

A Phase I Study of FOLFIRINOX Plus IPI-926, a Hedgehog Pathway Inhibitor, for Advanced Pancreatic Adenocarcinoma.

OBJECTIVES: In mouse models of pancreatic cancer, IPI-926, an oral Hedgehog inhibitor, increases chemotherapy delivery by depleting tumor-associated stroma. This multicenter phase Ib study evaluated IPI-926 in combination with FOLFIRINOX (5-fluorouracil, leucovorin, irinotecan, oxaliplatin) in patients with advanced pancreatic cancer. METHODS: Patients were treated with once-daily IPI-926 plus FOLFIRINOX. A 3 + 3 dose escalation design was used, with cohort expansion at the maximum tolerated dose. A subset of patients underwent perfusion computed tomography to assess changes in tumor perfusion. RESULTS: The maximum tolerated dose was identified 1 dose level below standard FOLFIRINOX. Common treatment-related adverse events included liver function test abnormalities, neuropathy, nausea/vomiting, and diarrhea. Objective response rate was high (67%), and patients receiving IPI-926 maintenance showed further declines in CA19-9 levels even after FOLFIRINOX discontinuation. Treatment did not result in consistent increases in tumor perfusion. The study closed early when a separate phase II trial of IPI-926 plus gemcitabine indicated detrimental effects of this combination. CONCLUSIONS: This is the first study to demonstrate the feasibility of using FOLFIRINOX as the chemotherapeutic backbone in a clinical trial design. Although robust antitumor activity and acceptable safety were observed with the addition of IPI-926 to this regimen, future development of Hedgehog inhibitors in pancreatic cancer seems unlikely.

Accuracy of facial soft tissue thickness measurements in personal computer-based multiplanar reconstructed computed tomographic images.

The purpose of this study was to determine the precision and accuracy of facial soft tissue measurement using personal computer (PC)-based multiplanar reconstructed (MPR) computed tomography (CT) images and to evaluate the effect of the various CT scanning protocols on the facial soft tissue thickness measurement. Thirteen different CT imaging protocols were used to image a cadaver head. MPR reformations and three-dimensional (3D) reconstructions viewed on a laptop PC were used to make measurements at six specific sites on each set of images. These measurements were compared to physical measurements at the same sites. Increasing the slice thickness resulted in decreased image quality. Within the same slice thickness, increasing the pitch ratio in the spiral mode, resulted in decreasing image quality. The image quality of conventional CT scanning was relatively poorer than that of the spiral CT scanning. However, the mean deviation from the physical measurement was within 0.43 mm in every instance. This mean deviation was quite small and clinically acceptable for measuring the soft tissue thickness of the facial area. PC-based MPR CT images of the face using routine scanning CT protocols can be used to accurately measure soft tissue thickness in the facial region. However, for more fine and accurate data collection, scanning protocols with slice thicknesses less than 5mm, and a spiral/helical mode pitch less than 2:1 are recommended.

Lung cancer-a fractal viewpoint.

Fractals are mathematical constructs that show self-similarity over a range of scales and non-integer (fractal) dimensions. Owing to these properties, fractal geometry can be used to efficiently estimate the geometrical complexity, and the irregularity of shapes and patterns observed in lung tumour growth (over space or time), whereas the use of traditional Euclidean geometry in such calculations is more challenging. The application of fractal analysis in biomedical imaging and time series has shown considerable promise for measuring processes as varied as heart and respiratory rates, neuronal cell characterization, and vascular development. Despite the advantages of fractal mathematics and numerous studies demonstrating its applicability to lung cancer research, many researchers and clinicians remain unaware of its potential. Therefore, this Review aims to introduce the fundamental basis of fractals and to illustrate how analysis of fractal dimension (FD) and associated measurements, such as lacunarity (texture) can be performed. We describe the fractal nature of the lung and explain why this organ is particularly suited to fractal analysis. Studies that have used fractal analyses to quantify changes in nuclear and chromatin FD in primary and metastatic tumour cells, and clinical imaging studies that correlated changes in the FD of tumours on CT and/or PET images with tumour growth and treatment responses are reviewed. Moreover, the potential use of these techniques in the diagnosis and therapeutic management of lung cancer are discussed.

Blind deblurring of spiral CT images-comparative studies on edge-to-noise ratios.

A recently developed blind deblurring algorithm based on the edge-to-noise ratio has been applied to improve the quality of spiral CT images. Since the discrepancy measure used to quantify the edge and noise effects is not symmetric, there are several ways to formulate the edge-to-noise ratio. This article is to investigate the performance of those ratios with phantom and patient data. In the phantom study, it is shown that all the ratios share similar properties, validating the blind deblurring algorithm. The image fidelity improvement varies from 29% to 33% for different ratios, according to the root mean square error (RMSE) criterion; the optimal iteration number determined for each ratio varies from 25 to 35. Those ratios that are associated with most satisfactory performance are singled out for the image fidelity improvement of about 33% in the numerical simulation. After automatic blind deblurring with the selected ratios, the spatial resolution of CT is substantially refined in all the cases tested.

Blind deblurring of spiral CT images.

To discriminate fine anatomical features in the inner ear, it has been desirable that spiral computed tomography (CT) may perform beyond their current resolution limits with the aid of digital image processing techniques. In this paper, we develop a blind deblurring approach to enhance image resolution retrospectively without complete knowledge of the underlying point spread function (PSF). An oblique CT image can be approximated as the convolution of an isotropic Gaussian PSF and the actual cross section. Practically, the parameter of the PSF is often unavailable. Hence, estimation of the parameter for the underlying PSF is crucially important for blind image deblurring. Based on the iterative deblurring theory, we formulate an edge-to-noise ratio (ENR) to characterize the image quality change due to deblurring. Our blind deblurring algorithm estimates the parameter of the PSF by maximizing the ENR, and deblurs images. In the phantom studies, the blind deblurring algorithm reduces image blurring by about 24%, according to our blurring residual measure. Also, the blind deblurring algorithm works well in patient studies. After fully automatic blind deblurring, the conspicuity of the submillimeter features of the cochlea is substantially improved.

Three-dimensional localization of cochlear implant electrodes using epipolar stereophotogrammetry.

Three-dimensional (3-D) localization of individual cochlear implant electrodes within the inner ear is of importance for modeling the electrical field of the cochlea, designing the electrode array, and programming the associated speech processor. A 3-D reconstruction method of cochlear implant electrodes is proposed to localize individual electrodes from two X-ray views in combination with the spiral computed tomography technique. By adapting epipolar geometry to the configuration of an X-ray imaging system, we estimate individual electrode locations in the least square sense without using a patient attachment required by an existing stereophotogrammetry technique. Furthermore, our method does not require any knowledge of the intrinsic and extrinsic parameters of the imaging system. The performance of our method is studied in numerical simulation and with patient data and is found to be sufficiently accurate for clinical use. The maximum root mean-square errors measured are 0.0445 and 0.214 mm for numerical simulation and patient data, respectively.