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Arrays of radiofrequency coils are widely used in magnetic resonance imaging to achieve high signal-to-noise ratios and flexible volume coverage, to accelerate scans using parallel reception, and to mitigate field non-uniformity using parallel transmission. However, conventional coil arrays require complex decoupling technologies to reduce electromagnetic coupling between coil elements, which would otherwise amplify noise and limit transmitted power. Here we report a novel self-decoupled RF coil design with a simple structure that requires only an intentional redistribution of electrical impedances around the length of the coil loop. We show that self-decoupled coils achieve high inter-coil isolation between adjacent and non-adjacent elements of loop arrays and mixed arrays of loops and dipoles. Self-decoupled coils are also robust to coil separation, making them attractive for size-adjustable and flexible coil arrays.
Within the artery intima, endothelial cells respond to mechanical cues and changes in subendothelial matrix stiffness. Recently, we found that the aging subendothelial matrix stiffens heterogeneously and that stiffness heterogeneities are present on the scale of one cell length. However, the impacts of these complex mechanical micro-heterogeneities on endothelial cells have not been fully understood. Here, we simulate the effects of matrices that mimic young and aged vessels on single- and multi-cell endothelial cell models and examine the resulting cell basal strain profiles. Although there are limitations to the model which prohibit the prediction of intracellular strain distributions in alive cells, this model does introduce mechanical complexities to the subendothelial matrix material. More heterogeneous basal strain distributions are present in the single- and multi-cell models on the matrix mimicking an aged artery over those exhibited on the young artery. Overall, our data indicate that increased heterogeneous strain profiles in endothelial cells are displayed in silico when there is an increased presence of microscale arterial mechanical heterogeneities in the matrix.
Drug-induced cardiovascular complications are the most common adverse drug events and account for the withdrawal or severe restrictions on the use of multitudinous postmarketed drugs. In this study, we developed new in silico models for systematic identification of drug-induced cardiovascular complications in drug discovery and postmarketing surveillance. Specifically, we collected drug-induced cardiovascular complications covering the five most common types of cardiovascular outcomes (hypertension, heart block, arrhythmia, cardiac failure, and myocardial infarction) from four publicly available data resources: Comparative Toxicogenomics Database, SIDER, Offsides, and MetaADEDB. Using these databases, we developed a combined classifier framework through integration of five machine-learning algorithms: logistic regression, random forest, k-nearest neighbors, support vector machine, and neural network. The totality of models included 180 single classifiers with area under receiver operating characteristic curves (AUC) ranging from 0.647 to 0.809 on 5-fold cross-validations. To develop the combined classifiers, we then utilized a neural network algorithm to integrate the best four single classifiers for each cardiovascular outcome. The combined classifiers had higher performance with an AUC range from 0.784 to 0.842 compared to single classifiers. Furthermore, we validated our predicted cardiovascular complications for 63 anticancer agents using experimental data from clinical studies, human pluripotent stem cell-derived cardiomyocyte assays, and literature. The success rate of our combined classifiers reached 87%. In conclusion, this study presents powerful in silico tools for systematic risk assessment of drug-induced cardiovascular complications. This tool is relevant not only in early stages of drug discovery but also throughout the life of a drug including clinical trials and postmarketing surveillance.
We have previously reported that the dispersion of spin-lattice relaxation rates in the rotating frame (R ) of tissue water protons at high field can be dominated by chemical exchange contributions. Ischemia in brain causes changes in tissue pH, which in turn may affect proton exchange rates. Amide proton transfer (APT, a form of chemical exchange saturation transfer) has been shown to be sensitive to chemical exchange rates and able to detect pH changes non-invasively following ischemic stroke. However, the specificity of APT to pH changes is decreased because of the influence of several other factors that affect magnetization transfer. R is less influenced by such confounding factors and thus may be more specific for detecting variations in pH. Here, we applied a spin-locking sequence to detect ischemic stroke in animal models. Although R images acquired with a single spin-locking amplitude (ω ) have previously been used to assess stroke, here we use ΔR , which is the difference in R values acquired with two different locking fields to emphasize selectively the contribution of chemical exchange effects. Numerical simulations with different exchange rates and measurements of tissue homogenates with different pH were performed to evaluate the specificity of ΔR to detect tissue acidosis. Spin-lock and APT data were acquired on five rat brains after ischemic strokes induced via middle cerebral artery occlusions. Correlations between these data were analyzed at different time points after the onset of stroke. The results show that ΔR (but not R acquired with a single ω ) was significantly correlated with APT metrics consistent with ΔR varying with pH.
Copyright © 2018 John Wiley & Sons, Ltd.
Biophysical models designed to predict the growth and response of tumors to treatment have the potential to become a valuable tool for clinicians in care of cancer patients. Specifically, individualized tumor forecasts could be used to predict response or resistance early in the course of treatment, thereby providing an opportunity for treatment selection or adaption. This chapter discusses an experimental and modeling framework in which noninvasive imaging data is used to initialize and parameterize a subject-specific model of tumor growth. This modeling approach is applied to an analysis of murine models of glioma growth.
The unique metabolic demands of cancer cells underscore potentially fruitful opportunities for drug discovery in the era of precision medicine. However, therapeutic targeting of cancer metabolism has led to surprisingly few new drugs to date. The neutral amino acid glutamine serves as a key intermediate in numerous metabolic processes leveraged by cancer cells, including biosynthesis, cell signaling, and oxidative protection. Herein we report the preclinical development of V-9302, a competitive small molecule antagonist of transmembrane glutamine flux that selectively and potently targets the amino acid transporter ASCT2. Pharmacological blockade of ASCT2 with V-9302 resulted in attenuated cancer cell growth and proliferation, increased cell death, and increased oxidative stress, which collectively contributed to antitumor responses in vitro and in vivo. This is the first study, to our knowledge, to demonstrate the utility of a pharmacological inhibitor of glutamine transport in oncology, representing a new class of targeted therapy and laying a framework for paradigm-shifting therapies targeting cancer cell metabolism.
Computational membrane protein design is challenging due to the small number of high-resolution structures available to elucidate the physical basis of membrane protein structure, multiple functionally important conformational states, and a limited number of high-throughput biophysical assays to monitor function. However, structural determination of membrane proteins has made tremendous progress in the past years. Concurrently the field of soluble computational design has made impressive inroads. These developments allow us to tackle the formidable challenge of designing functional membrane proteins. Herein, Rosetta is benchmarked for membrane protein design. We evaluate strategies to cope with the often reduced quality of experimental membrane protein structures. Further, we test the usage of symmetry in design protocols, which is particularly important as many membrane proteins exist as homo-oligomers. We compare a soluble scoring function with a scoring function optimized for membrane proteins, RosettaMembrane. Both scoring functions recovered around half of the native sequence when completely redesigning membrane proteins. However, RosettaMembrane recovered the most native-like amino acid property composition. While leucine was overrepresented in the inner and outer-hydrophobic regions of RosettaMembrane designs, it resulted in a native-like surface hydrophobicity indicating that it is currently the best option for designing membrane proteins with Rosetta.
© 2017 The Protein Society.
Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.
Spatiotemporal balancing of cellular proliferation and differentiation is crucial for postnatal tissue homoeostasis and organogenesis. During embryonic development, pancreatic progenitors simultaneously proliferate and differentiate into the endocrine, ductal and acinar lineages. Using in vivo clonal analysis in the founder population of the pancreas here we reveal highly heterogeneous contribution of single progenitors to organ formation. While some progenitors are bona fide multipotent and contribute progeny to all major pancreatic cell lineages, we also identify numerous unipotent endocrine and ducto-endocrine bipotent clones. Single-cell transcriptional profiling at E9.5 reveals that endocrine-committed cells are molecularly distinct, whereas multipotent and bipotent progenitors do not exhibit different expression profiles. Clone size and composition support a probabilistic model of cell fate allocation and in silico simulations predict a transient wave of acinar differentiation around E11.5, while endocrine differentiation is proportionally decreased. Increased proliferative capacity of outer progenitors is further proposed to impact clonal expansion.
Recent studies reveal that both phase aberration and reverberation play a major role in degrading ultrasound image quality. We previously developed an algorithm for suppressing clutter, but we have not yet tested it in the context of aberrated wavefronts. In this paper, we evaluate our previously reported algorithm, called aperture domain model image reconstruction (ADMIRE), in the presence of phase aberration and in the presence of multipath scattering and phase aberration. We use simulations to investigate phase aberration corruption and correction in the presence of reverberation. As part of this paper, we observed that ADMIRE leads to suppressed levels of aberration. In order to accurately characterize aberrated signals of interest, we introduced an adaptive component to ADMIRE to account for aberration, referred to as adaptive ADMIRE. We then use ADMIRE, adaptive ADMIRE, and conventional filtering methods to characterize aberration profiles on in vivo liver data. These in vivo results suggest that adaptive ADMIRE could be used to better characterize a wider range of aberrated wavefronts. The aberration profiles' full-width at half-maximum of ADMIRE, adaptive ADMIRE, and postfiltered data with 0.4- mm spatial cutoff frequency are 4.0 ± 0.28 mm, 2.8 ± 1.3 mm, and 2.8 ± 0.57 mm, respectively, while the average root-mean square values in the same order are 16 ± 5.4 ns, 20 ± 6.3 ns, and 19 ± 3.9 ns, respectively. Finally, because ADMIRE suppresses aberration, we perform a limited evaluation of image quality using simulations and in vivo data to determine how ADMIRE and adaptive ADMIRE perform with and without aberration correction.