Ureteroarterial Fistula

Ureteral-iliac artery fistula (UIAF) is a rare life threatening cause of hematuria. The increasing frequency is attributed to increasing use of ureteral stents. A 68-year-old female presented with gross hematuria. She had prior low anterior resection for rectal cancer and a retained ureteral stent. CT abdomen and pelvis showed a large recurrent pelvic mass and a retained stent. The patient underwent cystoscopy which showed a normal bladder. Upon removal of the stent, brisk bleeding was noted coming from the ureteral orifice. Antegrade pyelogram was done which revealed a UIAF. Angiography was done and a covered stent was placed. Multiple treatment options are available. All must consider management of the arterial and ureteral side. The arterial side may be addressed by primary open repair, embolization with extra-anatomic vascular reconstruction, or endovascular stenting. The ureter can be managed with nephroureterectomy, ureteral reconstruction, placement of a nephrostomy tube, or ureteral stenting. Being minimally invasive, we believe that endovascular stenting should be the preferred therapeutic option as it also corrects the source of bleeding while preserving distal blood flow

Colloidal Nanocrystals Embedded in Macrocrystals: Robustness, Photostability, and Color Purity

Cataloged from PDF version of article.The incorporation of colloidal quantum dots (QDs) into ionic crystals of various salts (NaCl, KCl, KBr, etc.) is demonstrated. The resulting mixed crystals of various shapes and beautiful colors preserve the strong luminescence of the incorporated QDs. Moreover, the ionic salts appear to be very tight matrices, ensuring the protection of the QDs from the environment and as a result providing them with extraordinary high photo- and chemical stability. A prototype of a white light-emitting diode (WLED) with a color conversion layer consisting of this kind of mixed crystals is demonstrated. These materials may also find applications in nonlinear optics and as luminescence standards

Brain microenvironment-driven resistance to immune and targeted therapies in acral melanoma.

BACKGROUND: Combination treatments targeting the MEK-ERK pathway and checkpoint inhibitors have improved overall survival in melanoma. Resistance to treatment especially in the brain remains challenging, and rare disease subtypes such as acral melanoma are not typically included in trials. Here we present analyses from longitudinal sampling of a patient with metastatic acral melanoma that became resistant to successive immune and targeted therapies. METHODS: We performed whole-exome sequencing and RNA sequencing on an acral melanoma that progressed on successive immune (nivolumab) and targeted (dabrafenib) therapy in the brain to identify resistance mechanisms. In addition, we performed growth inhibition assays, reverse phase protein arrays and immunoblotting on patient-derived cell lines using dabrafenib in the presence or absence of cerebrospinal fluid (CSF) in vitro. Patient-derived xenografts were also developed to analyse response to dabrafenib. RESULTS: Immune escape following checkpoint blockade was not due to loss of tumour cell recognition by the immune system or low neoantigen burden, but was associated with distinct changes in the microenvironment. Similarly, resistance to targeted therapy was not associated with acquired mutations but upregulation of the AKT/phospho-inositide 3-kinase pathway in the presence of CSF. CONCLUSION: Heterogeneous tumour interactions within the brain microenvironment enable progression on immune and targeted therapies and should be targeted in salvage treatments

Why decision support systems are important for medical education

During the last decades the inclusion of digital tools in health education has rapidly lead to a continuously enlarging digital era. All the online interactions between learners and tutors, the description, creation, reuse and sharing of educational digital resources and the interlinkage between them in conjunction with cheap storage technology has led to an enormous amount of educational data. Medical education is a unique type of education due to accuracy of information needed, continuous changing competences required and alternative methods of education used. Nowadays medical education standards provide the ground for organizing the educational data and the paradata. Analysis of such education data through education data mining techniques is in its infancy, but decision support systems for medical education need further research. To the best of our knowledge, there is a gap and a clear need for identifying the challenges for decision support systems in medical education in the era of medical education standards. Thus, in this paper the role and the attributes of such a decision support system for medical education are delineated and the challenges and vision for future actions are identified

Methods for characterising the steel–concrete interface to enhance understanding of reinforcement corrosion: a critical review by RILEM TC 262-SCI

The steel–concrete interface (SCI) is a complex, multi-phase and multi-scale system. It is widely known to influence the performance and long-term durability of concrete structures. However, a fundamental understanding of its properties and effects on corrosion initiation of embedded reinforcing steel remains elusive. This is attributed to its complicated heterogeneity and time-dependent nature, exacerbated by the lack of suitable techniques for systematic and detailed characterisation. This paper, prepared by members of the RILEM Technical Committee 262-SCI, critically reviews available information regarding current methods (laboratory or field-based) for characterising local properties of the SCI that have been identified as governing factors affecting corrosion initiation. These properties include characteristics of the steel such as mill scale and rust layers, and characteristics of the concrete such as interfacial voids, microstructure and moisture content. We evaluated over twenty methods and summarised their advantages, applications and limitations. The findings show a severe lack of well established, non-destructive techniques that are suitable for direct monitoring of the SCI at a representative scale with sufficiently high resolution (spatial, temporal), particularly for moisture related aspects. Several promising novel techniques with significant potential for further development and application were identified and discussed. Finally, we provide several recommendations for future research needs that are required to advance this critically important topic

Machine learning on normalized protein sequences

<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have been widely applied to biological sequences, e.g. to predict drug resistance in HIV-1 from sequences of drug target proteins and protein functional classes. As deletions and insertions are frequent in biological sequences, a major limitation of current methods is the inability to handle varying sequence lengths.</p> <p>Findings</p> <p>We propose to normalize sequences to uniform length. To this end, we tested one linear and four different non-linear interpolation methods for the normalization of sequence lengths of 19 classification datasets. Classification tasks included prediction of HIV-1 drug resistance from drug target sequences and sequence-based prediction of protein function. We applied random forests to the classification of sequences into "positive" and "negative" samples. Statistical tests showed that the linear interpolation outperforms the non-linear interpolation methods in most of the analyzed datasets, while in a few cases non-linear methods had a small but significant advantage. Compared to other published methods, our prediction scheme leads to an improvement in prediction accuracy by up to 14%.</p> <p>Conclusions</p> <p>We found that machine learning on sequences normalized by simple linear interpolation gave better or at least competitive results compared to state-of-the-art procedures, and thus, is a promising alternative to existing methods, especially for protein sequences of variable length.</p

Global patterns in endemicity and vulnerability of soil fungi

Fungi are highly diverse organisms, which provide multiple ecosystem services. However, compared with charismatic animals and plants, the distribution patterns and conservation needs of fungi have been little explored. Here, we examined endemicity patterns, global change vulnerability and conservation priority areas for functional groups of soil fungi based on six global surveys using a high-resolution, long-read metabarcoding approach. We found that the endemicity of all fungi and most functional groups peaks in tropical habitats, including Amazonia, Yucatan, West-Central Africa, Sri Lanka, and New Caledonia, with a negligible island effect compared with plants and animals. We also found that fungi are predominantly vulnerable to drought, heat and land-cover change, particularly in dry tropical regions with high human population density. Fungal conservation areas of highest priority include herbaceous wetlands, tropical forests, and woodlands. We stress that more attention should be focused on the conservation of fungi, especially root symbiotic arbuscular mycorrhizal and ectomycorrhizal fungi in tropical regions as well as unicellular early-diverging groups and macrofungi in general. Given the low overlap between the endemicity of fungi and macroorganisms, but high conservation needs in both groups, detailed analyses on distribution and conservation requirements are warranted for other microorganisms and soil organisms

Modeling of Ti-W Solidification Microstructures Under Additive Manufacturing Conditions

Additive manufacturing (AM) processes have many benefits for the fabrication of alloy parts, including the potential for greater microstructural control and targeted properties than traditional metallurgy processes. To accelerate utilization of this process to produce such parts, an effective computational modeling approach to identify the relationships between material and process parameters, microstructure, and part properties is essential. Development of such a model requires accounting for the many factors in play during this process, including laser absorption, material addition and melting, fluid flow, various modes of heat transport, and solidification. In this paper, we start with a more modest goal, to create a multiscale model for a specific AM process, Laser Engineered Net Shaping (LENS™), which couples a continuum-level description of a simplified beam melting problem (coupling heat absorption, heat transport, and fluid flow) with a Lattice Boltzmann-cellular automata (LB-CA) microscale model of combined fluid flow, solute transport, and solidification. We apply this model to a binary Ti-5.5 wt pct W alloy and compare calculated quantities, such as dendrite arm spacing, with experimental results reported in a companion paper

GÊNEROS DISCURSIVOS E ENSINO: UMA PROPOSTA DE APLICAÇÃO EM SALA DE AULA

Os gêneros discursivos são formas de agir e interagir discursivamente e são inerentes à comunicação humana. Neste artigo, nos propomos, a partir de um percurso teórico, discutir sobre o conceito de gênero discursivo com base nas reflexões de Bakhtin (2000) e Marcuschi (2003, 2005), considerando sua aplicabilidade no ensino como condição para assegurar à construção de conhecimentos fundamentais para as práticas sociais de linguagem. Para isso, refletimos sobre o gênero discursivo como atividade sociocomunicativa de interação social, produzido para as necessidades de comunicação, constituído de componentes sociais, históricos, culturais e cognitivos. Além disso, analisamos a sequência didática na perspectiva de Dolz e Schneuwly (2004) como possibilidade de auxiliar o ensino através dos gêneros.&nbsp; Entendemos ser essencial, por essa razão, que as aulas de língua portuguesa centrem-se, nos diferentes níveis de ensino, nas dinâmicas sociais de interação por meio dos gêneros discursivos. &nbsp;&nbsp; &nbsp;&nbsp; &nbsp