Macular and retinal dysfunction of unknown origin in adults with normal fundi: evidence for an autoimmune pathophysiology.

Here we report the discovery of and phenotypic characterization of a retinal disorder of unknown origin in adults using clinical, electrophysiological and psychophysical techniques, and to seek the presence of circulating retinal autoantibodies in the sera of these patients. Sixteen patients were identified with progressive bilateral visual loss over a period of months. Ten of the patients were male, and the average age was 55.3 years (range from 43 to 76 years). Known causes such as carcinoma-associated retinopathy, acute zonal occult outer retinopathy and hereditary cone dystrophy appeared unlikely. Investigations included electrophysiology, fundus autofluorescence imaging and psychophysical tests. The sera of these patients were analyzed with indirect immunocytochemistry and Western immunoblot analysis on murine (BALB/c) retinal tissue for the presence of retinal autoantibodies. Bilateral visual loss and photophobia progressed over a period of months to years (average 28.7 months, range 3-67) and subsequently stabilized. No abnormality was observed by biomicroscopy, angiography or autofluorescence imaging. Electrophysiology indicated predominant cone-system dysfunction, either macular or generalized, and post-phototransduction involvement in 9 patients (56%). Photopic and scotopic visual fields and dark adaptation kinetics showed both cone and rod system involvement in all cases. Heterogeneous immunohistochemical staining patterns were seen with the sera of these patients as compared with controls. A majority of the affected patients (9/15) stained with an antinuclear pattern. The retinal autoantibodies from the sera of most patients reacted with the retinal proteins of molecular weight between 34 and 40 kDa. The aetiology of this distinctive retinal disorder therefore appears to be mediated through an autoimmune mechanism.

A Small World Perspective on Urban Systems

The theory of small-world networks as initiated by Watts and Strogatz (1998) has drawn new insights in spatial analysis as well as systems theory. The theoryâeuro?s concepts and methods are particularly relevant to geography, where spatial interaction is mainstream and where interactions can be described and studied using large numbers of exchanges or similarity matrices. Networks are organized through direct links or by indirect paths, inducing topological proximities that simultaneously involve spatial, social, cultural or organizational dimensions. Network synergies build over similarities and are fed by complementarities between or inside cities, with the two effects potentially amplifying each other according to the âeurooepreferential attachmentâeuro hypothesis that has been explored in a number of different scientific fields (Barabási, Albert 1999; Barabási A-L 2002; Newman M, Watts D, Barabàsi A-L). In fact, according to Barabási and Albert (1999), the high level of hierarchy observed in âeurooescale-free networksâeuro results from âeurooepreferential attachmentâeuro, which characterizes the development of networks: new connections appear preferentially close to nodes that already have the largest number of connections because in this way, the improvement in the network accessibility of the new connection will likely be greater. However, at the same time, network regions gathering dense and numerous weak links (Granovetter, 1985) or network entities acting as bridges between several components (Burt 2005) offer a higher capacity for urban communities to benefit from opportunities and create future synergies. Several methodologies have been suggested to identify such denser and more coherent regions (also called communities or clusters) in terms of links (Watts, Strogatz 1998; Watts 1999; Barabási, Albert 1999; Barabási 2002; Auber 2003; Newman 2006). These communities not only possess a high level of dependency among their member entities but also show a low level of âeurooevulnerabilityâeuro, allowing for numerous redundancies (Burt 2000; Burt 2005). The SPANGEO project 2005âeuro"2008 (SPAtial Networks in GEOgraphy), gathering a team of geographers and computer scientists, has included empirical studies to survey concepts and measures developed in other related fields, such as physics, sociology and communication science. The relevancy and potential interpretation of weighted or non-weighted measures on edges and nodes were examined and analyzed at different scales (intra-urban, inter-urban or both). New classification and clustering schemes based on the relative local density of subgraphs were developed. The present article describes how these notions and methods contribute on a conceptual level, in terms of measures, delineations, explanatory analyses and visualization of geographical phenomena.

The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes.

Advanced neuroinformatics tools are required for methods of connectome mapping, analysis, and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration, and sharing. We have designed and implemented the Connectome Viewer Toolkit - a set of free and extensible open source neuroimaging tools written in Python. The key components of the toolkit are as follows: (1) The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. (2) The Connectome File Format Library enables management and sharing of connectome files. (3) The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration, and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http://www.cmtk.org/

Benefit of anti-HER2-coated paclitaxel-loaded immuno-nanoparticles in the treatment of disseminated ovarian cancer: Therapeutic efficacy and biodistribution in mice.

The benefit of polymeric immuno-nanoparticles (NPs-Tx-HER), consisting of paclitaxel (Tx)-loaded nanoparticles coated with anti-HER2 monoclonal antibodies (Herceptin, trastuzumab), in cancer treatment was assessed in a disseminated xenograft ovarian cancer model induced by intraperitoneal inoculation of SKOV-3 cells overexpressing HER2 antigens. The study was focused on the evaluation of therapeutic efficacy and biodistribution of NPs-Tx-HER compared to other Tx formulations. The therapeutic efficacy was determined by two methods: bioluminescence imaging and survival rate. The treatment regimen consisted in an initial dose of 20mg/kg Tx administered as 10mg/kg intravenously (IV) and 10mg/kg intraperitonealy (IP), followed by five alternative IP and IV injections of 10mg/kg Tx every 3 days. The bioluminescence study has clearly shown the superior anti-tumor activity of NPs-Tx-HER compared to free Tx. As a confirmation of these results, a significantly longer survival of mice was observed for NPs-Tx-HER treatment compared to free Tx, Tx-loaded nanoparticles coated with an irrelevant mAb (Mabthera, rituximab) or Herceptin alone, indicating the potential of immuno-nanoparticles in cancer treatment. The biodistribution pattern of Tx was assessed on healthy and tumor bearing mice after IV or IP administration. An equivalent biodistribution profile was observed in healthy mice for Tx encapsulated either in uncoated nanoparticles (NPs-Tx) or in NPs-Tx-HER. No significant difference in Tx biodistribution was observed after IV or IP injection, except for a lower accumulation in the lungs when NPs were administered by IP. Encapsulated Tx accumulated in the organs of the reticulo-endothelial system (RES) such as the liver and spleen, whereas free Tx had a non-specific distribution in all tested organs. Compared to free Tx, the single dose injection (IV or IP) of encapsulated Tx in mice bearing tumors induced a higher tumor accumulation. However, no difference in overall tumor accumulation between NPs-Tx-HER and NPs-Tx was observed. In conclusion, the encapsulation of Tx into NPs-Tx-HER immuno-nanoparticles resulted in an improved efficacy of drug in the treatment of disseminated ovarian cancer overexpressing HER2 receptors.

Migrated foreign body liver abscess: illustrative case report, systematic review, and proposed diagnostic algorithm.

Pyogenic liver abscess is a severe condition and a therapeutic challenge. Treatment failure may be due to an unrecognized ingested foreign body that migrated from the gastrointestinal tract. There has recently been a marked increase in the number of reported cases of this condition, but initial misdiagnosis as cryptogenic liver abscess still occurs in the majority of cases. We conducted the current study to characterize this entity and provide a diagnostic strategy applicable worldwide. To this end, data were collected from our case and from a systematic review that identified 59 well-described cases. Another systematic review identified series of cryptogenic-and Asian Klebsiella-liver abscess; these data were pooled and compared with the data from the cases of migrated foreign body liver abscess. The review points out the low diagnostic accuracy of history taking, modern imaging, and even surgical exploration. A fistula found through imaging procedures or endoscopy warrants surgical exploration. Findings suggestive of foreign body migration are symptoms of gastrointestinal perforation, computed tomography demonstration of a thickened gastrointestinal wall in continuity with the abscess, and adhesions seen during surgery. Treatment failure, left lobe location, unique location (that is, only 1 abscess location within the liver), and absence of underlying conditions also point to the diagnosis, as shown by comparison with the cryptogenic liver abscess series. This study demonstrates that migrated foreign body liver abscess is a specific entity, increasingly reported. It usually is not cured when unrecognized, and diagnosis is mainly delayed. This study provides what we consider the best available evidence for timely diagnosis with worldwide applicability. Increased awareness is required to treat this underestimated condition effectively, and further studies are needed.

Remorin, a solanaceae protein resident in membrane rafts and plasmodesmata, impairs potato virus X movement.

Remorins (REMs) are proteins of unknown function specific to vascular plants. We have used imaging and biochemical approaches and in situ labeling to demonstrate that REM clusters at plasmodesmata and in approximately 70-nm membrane domains, similar to lipid rafts, in the cytosolic leaflet of the plasma membrane. From a manipulation of REM levels in transgenic tomato (Solanum lycopersicum) plants, we show that Potato virus X (PVX) movement is inversely related to REM accumulation. We show that REM can interact physically with the movement protein TRIPLE GENE BLOCK PROTEIN1 from PVX. Based on the localization of REM and its impact on virus macromolecular trafficking, we discuss the potential for lipid rafts to act as functional components in plasmodesmata and the plasma membrane.

A realistic Computed Tomography simulator for small motion analysis of cerebral aneurysms.

This paper describes a realistic simulator for the Computed Tomography (CT) scan process for motion analysis. In fact, we are currently developing a new framework to find small motion from the CT scan. In order to prove the fidelity of this framework, or potentially any other algorithm, we present in this paper a simulator to simulate the whole CT acquisition process with a priori known parameters. In other words, it is a digital phantom for the motion analysis that can be used to compare the results of any related algorithm with the ground-truth realistic analytical model. Such a simulator can be used by the community to test different algorithms in the biomedical imaging domain. The most important features of this simulator are its different considerations to simulate the best the real acquisition process and its generality.

The Lausanne Institutional Biobank: a new resource to catalyse research in personalised medicine and pharmaceutical sciences.

Breakthrough technologies which now enable the sequencing of individual genomes will irreversibly modify the way diseases are diagnosed, predicted, prevented and treated. For these technologies to reach their full potential requires, upstream, access to high-quality biomedical data and samples from large number of properly informed and consenting individuals and, downstream, the possibility to transform the emerging knowledge into a clinical utility. The Lausanne Institutional Biobank was designed as an integrated, highly versatile infrastructure to harness the power of these emerging technologies and catalyse the discovery and development of innovative therapeutics and biomarkers, and advance the field of personalised medicine. Described here are its rationale, design and governance, as well as parallel initiatives which have been launched locally to address the societal, ethical and technological issues associated with this new bio-resource. Since January 2013, inpatients admitted at Lausanne CHUV University Hospital have been systematically invited to provide a general consent for the use of their biomedical data and samples for research, to complete a standardised questionnaire, to donate a 10-ml sample of blood for future DNA extraction and to be re-contacted for future clinical trials. Over the first 18 months of operation, 14,459 patients were contacted, and 11,051 accepted to participate in the study. This initial 18-month experience illustrates that a systematic hospital-based biobank is feasible; it shows a strong engagement in research from the patient population in this University Hospital setting, and the need for a broad, integrated approach for the future of medicine to reach its full potential.

Imagerie des atteintes inflammatoires rachidiennes [Imaging in inflammatory spine diseases]

There is a mean delay of 5 to 8 years between the onset of symptoms and the diagnosis of ankylosing spondylitis. This is due to the fact that radiographic sacroiliitis is delayed. The purpose of an earlier diagnosis is emphasized by the need for better management, the new diagnostic method including magnetic resonance imaging and by the efficacy of anti-TNF therapy. The current criteria are classification but not diagnostic criteria. Their sensitivity is insufficient for an early diagnosis of ankylosing spondylitis. MRI criteria allow to differentiate inflammatory signs from degenerative signs in patients sent for aspecific low back pain. The aims of this article are to illustrate the different stages of the disease from early inflammatory involvement to ankylosis and to discuss the role of imaging in the management of affected patients.

Double incretin receptor knock-out (DIRKO) mice present with alterations of trabecular and cortical micromorphology and bone strength.

A role for gut hormone in bone physiology has been suspected. We evidenced alterations of microstructural morphology (trabecular and cortical) and bone strength (both at the whole-bone - and tissue-level) in double incretin receptor knock-out (DIRKO) mice as compared to wild-type littermates. These results support a role for gut hormones in bone physiology. INTRODUCTION: The two incretins, glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), have been shown to control bone remodeling and strength. However, lessons from single incretin receptor knock-out mice highlighted a compensatory mechanism induced by elevated sensitivity to the other gut hormone. As such, it is unclear whether the bone alterations observed in GIP or GLP-1 receptor deficient animals resulted from the lack of a functional gut hormone receptor, or by higher sensitivity for the other gut hormone. The aims of the present study were to investigate the bone microstructural morphology, as well as bone tissue properties, in double incretin receptor knock-out (DIRKO) mice. METHODS: Twenty-six-week-old DIRKO mice were age- and sex-matched with wild-type (WT) littermates. Bone microstructural morphology was assessed at the femur by microCT and quantitative X-ray imaging, while tissue properties were investigated by quantitative backscattered electron imaging and Fourier-transformed infrared microscopy. Bone mechanical response was assessed at the whole-bone- and tissue-level by 3-point bending and nanoindentation, respectively. RESULTS: As compared to WT animals, DIRKO mice presented significant augmentations in trabecular bone mass and trabecular number whereas bone outer diameter, cortical thickness, and cortical area were reduced. At the whole-bone-level, yield stress, ultimate stress, and post-yield work to fracture were significantly reduced in DIRKO animals. At the tissue-level, only collagen maturity was reduced by 9 % in DIRKO mice leading to reductions in maximum load, hardness, and dissipated energy. CONCLUSIONS: This study demonstrated the critical role of gut hormones in controlling bone microstructural morphology and tissue properties.

The role of embolic protection devices during carotid stenting prior to cardiac surgery in asymptomatic patients: Empty filters?

Objectives: The purpose of this study was to analyze the debris captured in the distal protection filters used during carotid artery stenting (CAS). Background: CAS is an option available to high-risk patients requiring revascularization. Filters are suggested for optimal stroke prevention during CAS. Methods: From May 2005 to June 2007, filters from 59 asymptomatic patients who underwent CAS were collected and sent to a specialized laboratory for light-microscope and histological analysis. Peri- and postprocedural outcomes were assessed during 1-year follow-up. Results: On the basis of biomedical imaging of the filter debris, the captured material could not be identified as embolized particles from the carotid plaque. On histological analysis the debris consisted mainly of red blood cell aggregates and/ or platelets, occasionally accompanied by granulocytes. We found no consistent histological evidence of embolized particles originating from atherosclerotic plaques. Post-procedure, three neurological events were reported: two (3.4%) transient ischemic attacks (TIA) and one (1.7%) ipsilateral minor stroke. Conclusion: The filters used during CAS in asymptomatic patients planned for cardiac surgery often remained empty. These findings may be explained by assuming that asymptomatic patients feature a different atherosclerotic plaque composition or stabilization through antiplatelet medication. Larger, randomized trials are clearly warranted, especially in the asymptomatic population. © 2012 Wiley Periodicals, Inc.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Altering α-dystroglycan receptor affinity of LCMV pseudotyped lentivirus yields unique cell and tissue tropism.

BACKGROUND: The envelope glycoprotein of lymphocytic choriomeningitis virus (LCMV) can efficiently pseudotype lentiviral vectors. Some strains of LCMV exploit high affinity interactions with α-dystroglycan (α-DG) to bind to cell surfaces and subsequently fuse in low pH endosomes. LCMV strains with low α-DG affinity utilize an unknown receptor and display unique tissue tropisms. We pseudotyped non-primate feline immunodeficiency virus (FIV) vectors using LCMV derived glycoproteins with high or low affinity to α-DG and evaluated their properties in vitro and in vivo. METHODS: We pseudotyped FIV with the LCMV WE54 strain envelope glycoprotein and also engineered a point mutation in the WE54 envelope glycoprotein (L260F) to diminish α-DG affinity and direct binding to alternate receptors. We hypothesized that this change would alter in vivo tissue tropism and enhance gene transfer to neonatal animals. RESULTS: In mice, hepatic α- and β-DG expression was greatest at the late gestational and neonatal time points. When displayed on the surface of the FIV lentivirus the WE54 L260F mutant glycoprotein bound weakly to immobilized α-DG. Additionally, LCMV WE54 pseudotyped FIV vector transduction was neutralized by pre-incubation with soluble α-DG, while the mutant glycoprotein pseudotyped vector was not. In vivo gene transfer in adult mice with either envelope yielded low transduction efficiencies in hepatocytes following intravenous delivery. In marked contrast, neonatal gene transfer with the LCMV envelopes, and notably with the FIV-L260F vector, conferred abundant liver and lower level cardiomyocyte transduction as detected by luciferase assays, bioluminescent imaging, and β-galactosidase staining. CONCLUSIONS: These results suggest that a developmentally regulated receptor for LCMV is expressed abundantly in neonatal mice. LCMV pseudotyped vectors may have applications for neonatal gene transfer. ABBREVIATIONS: Armstrong 53b (Arm53b); baculovirus Autographa californica GP64 (GP64); charge-coupled device (CCD); dystroglycan (DG); feline immunodeficiency virus (FIV); glycoprotein precursor (GP-C); firefly luciferase (Luc); lymphocytic choriomeningitis virus (LCMV); nuclear targeted β-galactosidase (ntLacZ); optical density (OD); PBS/0.1% (w/v) Tween-20 (PBST); relative light units (RLU); Rous sarcoma virus (RSV); transducing units per milliliter (TU/ml); vesicular stomatitis virus (VSV-G); wheat germ agglutinin (WGA); 50% reduction in binding (C50).

Molecular imaging in the development of a novel treatment paradigm for glioblastoma (GBM): an integrated multidisciplinary commentary.

Current therapeutic strategies against glioblastoma (GBM) have failed to prevent disease progression and recurrence effectively. The part played by molecular imaging (MI) in the development of novel therapies has gained increasing traction in recent years. For the first time, using expertise from an integrated multidisciplinary group of authors, herein we present a comprehensive evaluation of state-of-the-art GBM imaging and explore how advances facilitate the emergence of new treatment options. We propose a novel next-generation treatment paradigm based on the targeting of multiple hallmarks of cancer evolution that will heavily rely on MI.

Free-breathing whole-heart coronary MRA with 3D radial SSFP and self-navigated image reconstruction.

Respiratory motion is a major source of artifacts in cardiac magnetic resonance imaging (MRI). Free-breathing techniques with pencil-beam navigators efficiently suppress respiratory motion and minimize the need for patient cooperation. However, the correlation between the measured navigator position and the actual position of the heart may be adversely affected by hysteretic effects, navigator position, and temporal delays between the navigators and the image acquisition. In addition, irregular breathing patterns during navigator-gated scanning may result in low scan efficiency and prolonged scan time. The purpose of this study was to develop and implement a self-navigated, free-breathing, whole-heart 3D coronary MRI technique that would overcome these shortcomings and improve the ease-of-use of coronary MRI. A signal synchronous with respiration was extracted directly from the echoes acquired for imaging, and the motion information was used for retrospective, rigid-body, through-plane motion correction. The images obtained from the self-navigated reconstruction were compared with the results from conventional, prospective, pencil-beam navigator tracking. Image quality was improved in phantom studies using self-navigation, while equivalent results were obtained with both techniques in preliminary in vivo studies.