Retinal boundary segmentation in stargardt disease optical coherence tomography images using automated deep learning

Purpose: To use a deep learning model to develop a fully automated method (fully semantic network and graph search [FS-GS]) of retinal segmentation for optical coherence tomography (OCT) images from patients with Stargardt disease. Methods: Eighty-seven manually segmented (ground truth) OCT volume scan sets (5171 B-scans) from 22 patients with Stargardt disease were used for training, validation and testing of a novel retinal boundary detection approach (FS-GS) that combines a fully semantic deep learning segmentation method, which generates a per-pixel class prediction map with a graph-search method to extract retinal boundary positions. The performance was evaluated using the mean absolute boundary error and the differences in two clinical metrics (retinal thickness and volume) compared with the ground truth. The performance of a separate deep learning method and two publicly available software algorithms were also evaluated against the ground truth. Results: FS-GS showed an excellent agreement with the ground truth, with a boundary mean absolute error of 0.23 and 1.12 pixels for the internal limiting membrane and the base of retinal pigment epithelium or Bruch's membrane, respectively. The mean difference in thickness and volume across the central 6 mm zone were 2.10 µm and 0.059 mm3. The performance of the proposed method was more accurate and consistent than the publicly available OCTExplorer and AURA tools. Conclusions: The FS-GS method delivers good performance in segmentation of OCT images of pathologic retina in Stargardt disease. Translational Relevance: Deep learning models can provide a robust method for retinal segmentation and support a high-throughput analysis pipeline for measuring retinal thickness and volume in Stargardt disease

Oral candidiasis in Chikungunya viral fever: a case report

A 32 year old Indian male patient presented with chief complaints of a high fever, erythema on ear, severe polyarthritic joint pains & swelling, non pitting pedal oedema, facial puffiness and itching for past four days. He had no significant past medical and drug history and was serologically confirmed to have Chikungunya. Oral cavity inspection revealed whitish non erythematous pseudo membranous plaques on the hard palate, buccal surface of cheek and the floor of the mouth which was later microbiologically confirmed as Candidiasis. He tested negative for HIV and had leucopenia with severe CD4 T-lymphocytopenia. This is the first report of an opportunistic infection with CD4 T-lymphocytopaenia in Chikungunya fever

Towards Open and Equitable Access to Research and Knowledge for Development

Leslie Chan and colleagues discuss the value of open access not just for access to health information, but also for transforming structural inequity in current academic reward systems and for valuing scholarship from the South

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

Development of superlattice CrNNbN coatings for joint replacements deposited by High Power Impulse Magnetron Sputtering

The demand for reliable coating on medical implants is ever growing. In this research, enhanced performance of medical implants was achieved by a CrN/NbN coating utilising nanoscale multilayer/superlattice structure. The advantages of the novel High Power Impulse Magnetron Sputtering technology, namely its unique highly ionised plasma were exploited to deposit dense and strongly adherent coatings on Co-Cr implants. TEM analyses revealed coating superlattice structure with bi-layer thickness of 3.5 nm. CrN/NbN deposited on Co-Cr samples showed exceptionally high adhesion, critical load values of LC2= 50 N in scratch adhesion tests. Nanoindentation tests showed high hardness of 34 GPa and Young's modulus of 447 GPa. Low coefficient of friction (µ) 0.49 and coating wear coefficient (KC) = 4.94 x 10-16 m3N-1m-1 were recorded in dry sliding tests. Metal ion release studies showed a reduction in Co, Cr and Mo release at physiological and elevated temperatures, (70 oC) to almost undetectable levels (<1 ppb). Rotating beam fatigue testing showed a significant increase in fatigue strength from 349±59 MPa (uncoated) to 539±59 MPa (coated). In vitro biological testing has been performed in order to assess the safety of the coating in biological environment, cytotoxicity, genotoxicity and sensitisation testing have been performed, all showing no adverse effects. Keywords: Orthopaedic implant, High Power Impulse Magnetron Sputtering, Superlattice coating, Corrosion, Biocompatibility

Anti-inflammatory effect of bee pollen ethanol extract from Cistus sp. of Spanish on carrageenan-induced rat hind paw edema

<p>Abstract</p> <p>Background</p> <p>Bee pollen, a honeybee product, is the feed for honeybees prepared themselves by pollens collecting from plants and has been consumed as a perfect food in Europe, because it is nutritionally well balanced. In this study, we aimed to investigate the anti-inflammatory effect of bee pollen from <it>Cistus </it>sp. of Spanish origin by a method of carrageenan-induced paw edema in rats, and to investigate the mechanism of anti-inflammatory action and also to elucidate components involved in bee pollen extracted with ethanol.</p> <p>Methods</p> <p>The bee pollen bulk, its water extract and its ethanol extract were administered orally to rats. One hour later, paw edema was produced by injecting of 1% solution of carrageenan, and paw volume was measured before and after carrageenan injection up to 5 h. The ethanol extract and water extract were measured COX-1 and COX-2 inhibitory activities using COX inhibitor screening assay kit, and were compared for the inhibition of NO production in LPS-stimulated RAW 264.7 cells. The constituents of bee pollen were purified from the ethanol extract subjected to silica gel or LH-20 column chromatography. Each column chromatography fractions were further purified by repeated ODS or silica gel column chromatography.</p> <p>Results</p> <p>The bee pollen bulk mildly suppressed the carrageenan-induced paw edema and the water extract showed almost no inhibitory activity, but the ethanol extract showed relatively strong inhibition of paw edema. The ethanol extract inhibited the NO production and COX-2 but not COX-1 activity, but the water extract did not affect the NO production or COX activities. Flavonoids were isolated and purified from the ethanol extract of bee pollen, and identified at least five flavonoids and their glycosides.</p> <p>Conclusions</p> <p>It is suggested that the ethanol extract of bee pollen show a potent anti-inflammatory activity and its effect acts <it>via </it>the inhibition of NO production, besides the inhibitory activity of COX-2. Some flavonoids included in bee pollen may partly participate in some of the anti-inflammatory action. The bee pollen would be beneficial not only as a dietary supplement but also as a functional food.</p

Genetic Determinants of Phosphate Response in Drosophila

Phosphate is required for many important cellular processes and having too little phosphate or too much can cause disease and reduce life span in humans. However, the mechanisms underlying homeostatic control of extracellular phosphate levels and cellular effects of phosphate are poorly understood. Here, we establish Drosophila melanogaster as a model system for the study of phosphate effects. We found that Drosophila larval development depends on the availability of phosphate in the medium. Conversely, life span is reduced when adult flies are cultured on high phosphate medium or when hemolymph phosphate is increased in flies with impaired Malpighian tubules. In addition, RNAi-mediated inhibition of MAPK-signaling by knockdown of Ras85D, phl/D-Raf or Dsor1/MEK affects larval development, adult life span and hemolymph phosphate, suggesting that some in vivo effects involve activation of this signaling pathway by phosphate. To identify novel genetic determinants of phosphate responses, we used Drosophila hemocyte-like cultured cells (S2R+) to perform a genome-wide RNAi screen using MAPK activation as the readout. We identified a number of candidate genes potentially important for the cellular response to phosphate. Evaluation of 51 genes in live flies revealed some that affect larval development, adult life span and hemolymph phosphate levels

Quantum Algorithms for the Approximate <i>k</i>-List Problem and their Application to Lattice Sieving

The Shortest Vector Problem (SVP) is one of the mathematical foundations of lattice based cryptography. Lattice sieve algorithms are amongst the foremost methods of solving SVP. The asymptotically fastest known classical and quantum sieves solve SVP in a $d$-dimensional lattice in 2^{\const d + \smallo(d)} time steps with 2^{\const' d + \smallo(d)} memory for constants $c, c'$. In this work, we give various quantum sieving algorithms that trade computational steps for memory.We first give a quantum analogue of the classical $k$-Sieve algorithm [Herold--Kirshanova--Laarhoven, PKC'18] in the Quantum Random Access Memory (QRAM) model, achieving an algorithm that heuristically solves SVP in $2^{0.2989d + o(d)}$ time steps using $2^{0.1395d + o(d)}$ memory. This should be compared to the state-of-the-art algorithm [Laarhoven, Ph.D Thesis, 2015] which, in the same model, solves SVP in $2^{0.2653d + o(d)}$ time steps and memory. In the QRAM model these algorithms can be implemented using \poly(d) width quantum circuits.Secondly, we frame the $k$-Sieve as the problem of $k$-clique listing in a graph and apply quantum $k$-clique finding techniques to the $k$-Sieve. Finally, we explore the large quantum memory regime by adapting parallel quantum search [Beals et al., Proc. Roy. Soc. A'13] to the $2$-Sieve and giving an analysis in the quantum circuit model. We show how to heuristically solve SVP in $2^{0.1037d + o(d)}$ time steps using $2^{0.2075d + o(d)}$ quantum memory