Endovascular native vessel recanalization to maintain limb perfusion after infected prosthetic vascular graft excision

Prosthetic vascular graft infection is an uncommon yet serious condition. Traditional management has included debridement, excision of the infected graft, and revascularization as needed. We report on two cases in which limb viability was maintained by using endovascular native vessel recanalization after excision of infected prosthetic grafts. This approach was successful in maintaining adequate limb perfusion in both cases. Endovascular native vessel recanalization should be considered as an option to maintain limb viability after excision of infected prosthetic vascular grafts, especially when autogenous conduit is lacking or limitation of the extent of surgery is desirable

A Deep Learning based Fast Signed Distance Map Generation

Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters. The proposed SDM Neural Network generates a cochlea signed distance map depending on four input parameters and we show that the deep learning approach leads to a 60 fold improvement in the time of computation compared to more classical SDM generation methods. Therefore, the proposed approach achieves a good trade-off between accuracy and efficiency

Remobilization of Tol2 transposons in Xenopus tropicalis

<p>Abstract</p> <p>Background</p> <p>The Class II DNA transposons are mobile genetic elements that move DNA sequence from one position in the genome to another. We have previously demonstrated that the naturally occurring <it>Tol2 </it>element from <it>Oryzias latipes </it>efficiently integrates its corresponding non-autonomous transposable element into the genome of the diploid frog, <it>Xenopus tropicalis. Tol2 </it>transposons are stable in the frog genome and are transmitted to the offspring at the expected Mendelian frequency.</p> <p>Results</p> <p>To test whether <it>Tol2 </it>transposons integrated in the <it>Xenopus tropicalis </it>genome are substrates for remobilization, we injected <it>in vitro </it>transcribed <it>Tol2 </it>mRNA into one-cell embryos harbouring a single copy of a <it>Tol2 </it>transposon. Integration site analysis of injected embryos from two founder lines showed at least one somatic remobilization event per embryo. We also demonstrate that the remobilized transposons are transmitted through the germline and re-integration can result in the generation of novel GFP expression patterns in the developing tadpole. Although the parental line contained a single <it>Tol2 </it>transposon, the resulting remobilized tadpoles frequently inherit multiple copies of the transposon. This is likely to be due to the <it>Tol2 </it>transposase acting in discrete blastomeres of the developing injected embryo during the cell cycle after DNA synthesis but prior to mitosis.</p> <p>Conclusions</p> <p>In this study, we demonstrate that single copy <it>Tol2 </it>transposons integrated into the <it>Xenopus tropicalis </it>genome are effective substrates for excision and random re-integration and that the remobilized transposons are transmitted through the germline. This is an important step in the development of 'transposon hopping' strategies for insertional mutagenesis, gene trap and enhancer trap screens in this highly tractable developmental model organism.</p

Realistic Simulation of Electric Potential Distributions of Different Stimulation Modes in an Implanted Cochlea

International audienceSimulation of the intracochlear potentials is an important approach to study the activation of auditory nerve fibers under electrical stimulations.However, it is still unclear to which extent the simulation results are affected by precision in reproducing the exact cochlear geometry.In this study, we address to this question by comparing the actual electric potential measured from implanted human specimen with the simulationoutputs from two different parametric 3D cochlear models. One of the model is created from the default values[1] while the other is adapted to the micro-CT scan data of the implanted cochlea

In situ validation of a parametric model of electrical field distribution in an implanted cochlea

International audienceCochlear implants have been proved to be an effective treatment for patients with sensorineural hearing loss. Among all the approaches that have been developed to design better cochlear implants, 3D model-based simulation stands out due to its detailed description of the electric field which helps reveal the electrophysiological phenomena inside the cochlea. With the advances in the cochlear implant manufacturing technology, the requirement on simulation accuracy increases. Improving the simulation accuracy relies on two aspects: 1) a better geometrical description of the cochlea that is able to distinguish the subtle differences across patients; 2) a comprehensive and reliable validation of the created 3D model. In this paper, targeting at high precision simulation, we propose a parametric cochlea model which uses micro-CT images to adapt to different cochlea geometries, then demonstrate its validation process with multi-channel stimulation data measured from a implanted cochlea. Comparisons between the simulation and validation data show a good match under a variety of stimulation configurations. The results suggest that the electric field distribution is affected by the geometric characteristics of each individual cochlea. These differences can be correctly reflected by simulations based on a 3D model tuned with personalized data

Eye Movement Patterns Can Distinguish Schizophrenia From the Major Affective Disorders and Healthy Control Subjects

Open Access under the OUP Agreement Funding This project was supported by the following grants: The Royal Society of London, Chief Scientist Office Scotland (CZB/4/734), NHS Grampian Tenovus Scotland (G12/31), NHS Grampian Endowment Fund, Miller MacKenzie Trust, EU-FP6 (SGENE) and Health Innovation Challenge Fund, jointly from Wellcome Trust and Department of Health (WT-103911/Z/14/Z). The funders had no role in the original study design, the ongoing data collection and analysis, interpretation, or writing of the manuscript. We thank all who helped with clinical aspects of the study including research assistants Barbara Duff, Kate Cotton, Foteini Okonomitsiou, Elizabeth Hannaford, Zsuszanna Nemeth and Joanna Rodzinko Paska as well as the patients and volunteers whose help was indispensable. P Benson and D St Clair are co-founders of SACCADE Diagnostics Ltd a spin out company tasked to develop eye movement technology to assist diagnosis of major mental health disorders. The University of Aberdeen has patents pending in Europe (PCT/GB2013/050016) and USA (14/370,611). The data reported in this paper arose solely from funding by the acknowledged UK research bodies and charities none of whom have vested interests in the company. David St Clair had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Design of study and development of protocols: D St Clair, P Benson and S Beedie. Recruitment of patients, case note review and clinical and eye movement data collection, quality control and feature extraction of eye movement variables: St Clair, Rujescu, MacIntosh, Beedie, Lemmon, Nouzova. Drafting of the manuscript: St Clair and Nath. Critical revision of manuscript for important intellectual content: all authors. Statistical analyses: Nath and Benson. Interpretation of results: Nath, Benson, MacLennan and St Clair. Obtained funding: St Clair, Benson, MacIntosh, Rujescu. Supervision: St Clair, Benson, MacIntosh, Nath, Rujescu.Peer reviewedPublisher PD

Evaluation of the current distribution of the hybrid common ground stimulation in cochlear implants

International audienceBackgroundIn cochlear implants, the hybrid common ground is a combination of a classic monopolarstimulation with a standard common ground. This new stimulation montage allows the current toreturn from both the non-stimulating electrodes on the electrode array and the reference electrodeplaced between the skull and scalp. In theory, this lead to reach a compromise between the currentfocality and the efficiency of the stimulation. Even if this stimulation type has already been adoptedby some implant manufacturers, the 3D geometry of its current pathways remains to be studied.MethodsThe study is two-fold. First, an in-vitro experiment aimed to measure the electrical field producedby the hybrid common ground stimulation. An electrode array of an XP implant (Oticon Medical,Neurelec) was placed in saline solution and the electrical field was recorded by a probe that movesalong the programmed grid. During the stimulation, the current waveforms on all the groundingelectrodes were also recorded. Second, an in-situ measurement was conducted. Another XP devicewas implanted into a human specimen. The same procedure as in the in-vitro measurement wasperformed to record, this time, the current waveforms only.ResultsThe recorded two groups of current data were compared with each other to investigate how thecurrent path is modified by the geometry of a human cochlea. The potential distribution measuredduring the in-vitro experiment was also compared with other stimulation types such as monopolar.The energy consumption of the stimulation was also computed from the collected data

Remobilization of Sleeping Beauty transposons in the germline of Xenopus tropicalis

<p>Abstract</p> <p>Background</p> <p>The <it>Sleeping Beauty </it>(<it>SB</it>) transposon system has been used for germline transgenesis of the diploid frog, <it>Xenopus tropicalis</it>. Injecting one-cell embryos with plasmid DNA harboring an <it>SB </it>transposon substrate together with mRNA encoding the <it>SB </it>transposase enzyme resulted in non-canonical integration of small-order concatemers of the transposon. Here, we demonstrate that <it>SB </it>transposons stably integrated into the frog genome are effective substrates for remobilization.</p> <p>Results</p> <p>Transgenic frogs that express the <it>SB</it>10 transposase were bred with <it>SB </it>transposon-harboring animals to yield double-transgenic 'hopper' frogs. Remobilization events were observed in the progeny of the hopper frogs and were verified by Southern blot analysis and cloning of the novel integrations sites. Unlike the co-injection method used to generate founder lines, transgenic remobilization resulted in canonical transposition of the <it>SB </it>transposons. The remobilized <it>SB </it>transposons frequently integrated near the site of the donor locus; approximately 80% re-integrated with 3 Mb of the donor locus, a phenomenon known as 'local hopping'.</p> <p>Conclusions</p> <p>In this study, we demonstrate that <it>SB </it>transposons integrated into the <it>X. tropicalis </it>genome are effective substrates for excision and re-integration, and that the remobilized transposons are transmitted through the germline. This is an important step in the development of large-scale transposon-mediated gene- and enhancer-trap strategies in this highly tractable developmental model system.</p

Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks

International audienceMetal Artifacts creates often difficulties for a high-quality visual assessment of post-operative imaging in computed tomography (CT). A vast body of methods have been proposed to tackle this issue, but these methods were designed for regular CT scans and their performance is usually insufficient when imaging tiny implants. In the context of post-operative high-resolution CT imaging, we propose a 3D metal artifact reduction algorithm based on a generative adversarial neural network. It is based on the simulation of physically realistic CT metal artifacts created by cochlea implant electrodes on preoperative images. The generated images serve to train 3D generative adversarial networks for artifacts reduction. The proposed approach was assessed qualitatively and quantitatively on clinical conventional and cone-beam CT of cochlear implant postoperative images. These experiments show that the proposed method outperforms other general metal artifact reduction approaches

A Deep Learning based Fast Signed Distance Map Generation

International audienceSigned distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters. The proposed SDM Neural Network generates a cochlea signed distance map depending on four input parameters and we show that the deep learning approach leads to a 60 fold improvement in the time of computation compared to more classical SDM generation methods. Therefore, the proposed approach achieves a good trade-off between accuracy and efficiency