PARADOXICAL REACTIONS, ESPECIALLY PSORIASIS IN RHEUMATOLOGY PATIENTS RECEIVING BIOLOGIC THERAPY FROM THE TREASURE DATABASE: A 5-YEAR FOLLOW-UP STUDY

EULAR European Congress of Rheumatology (EULAR) -- JUN 01-04, 2022 -- Copenhagen, DENMARK[Abstract Not Available]European Alliance Assoc Rheumato

The mechanism of the air-jet texturing: the role of wetting, spin finish and friction in forming and fixing loops

A comprehensive review of the roles played by the airflow, wetting and spin finish on the air-jet texturing process is given. An experimental investigation of the air-jet texturing process using residual spin finish, yarn-to-yarn static and kinetic friction, filament strength, filament diameter, and on-line tension measurements and high-speed cine-photography is reported. Filament yarn motion in different regions of the texturing nozzle during dry and wet texturing is analysed. It is found that water acts as lubricant to reduce friction between the filaments in the wet texturing process as the filament yarn travels through the nozzle enabling easier relative motion of the filaments resulting in enhanced entanglement. Wet texturing also reduces spin finish on the yarn surface, which in turn, causes an increase in static friction between the filaments of the textured yarn resulting in better fixing of the loops and consequently superior yarns

EPIDEMIOLOGICAL CHARACTERISTICS OF VIRAL HEPATITIS IN PATIENTS WITH RHEUMATIC DISEASES - IMPLICATIONS FROM TREASURE DATABASE

EULAR European Congress of Rheumatology (EULAR) -- JUN 01-04, 2022 -- Copenhagen, DENMARK[Abstract Not Available]European Alliance Assoc Rheumato

Prevalence of leishmaniosis in dogs in Istanbul,Turkey determined by using PCR

Leishmania infantum is widespread in Mediterranean countries including Turkey and can cause a serious disease in both humans and dogs. Dogs are considered to be its main reservoirs. The current epidemiological study was carried out in Istanbul for detection of leishmaniosis among dogs. A total of 246 dogs were included in the study. Twenty one (8.54%) blood samples were found to be positive based on PCR diagnosis, using primers specific for the kinetoplast DNA of Leishmania. Infection rate was 6.51% in 169 dogs living in shelters and 12.99% in 77 client-owned dogs. The rate was significantly (p<0.001) higher in the dogs (37.93%) presenting one or more clinical symptoms which may be attributable to leishmaniosis, than in the asymptomatic dogs (4.61%). Eleven (52.4%) of 21 PCR positive dogs presented clinical symptoms whereas the rest of the dogs (47.62%) were asymptomatic. The major clinical sign in PCR positive animals was dermatological problems. Amongst PCR positive dogs, skin lesions were present in 11, weight loss in 2, lymphadenopathy in 2, epistaxis in 2 and lethargy in 2 dogs

A multidisciplinary approach to address climate-resilience, conservation and comfort in traditional architecture: The PROT3CT example

Traditional dwellings despite their environmental credentials, due to age, previous damage, and residents unable to afford even the limited maintenance allowed by restrictive legal framework, may offer poor thermal performance, which is expected to be further exacerbated by changing climate. More than 70% of Turkey’s built heritage stock is composed of traditional dwellings, which makes this stock able to create a major impact nationally on the building-related energy use, carbon emissions and population wellbeing. This research aims to develop an evidence-based multidisciplinary methodology for cost-effective retrofit of the traditional dwellings in Turkey, to improve energy performance, satisfy user expectations of comfort, and protect heritage value

Multiscale Feature Analysis of Salivary Gland Branching Morphogenesis

Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We here show how to define and calculate a multiscale feature set as an effective computational approach to identify and quantify changes at multiple biological scales and to distinguish between different states in developing tissues

Quantitative metric profiles capture three-dimensional temporospatial architecture to discriminate cellular functional states

<p>Abstract</p> <p>Background</p> <p>Computational analysis of tissue structure reveals sub-visual differences in tissue functional states by extracting quantitative signature features that establish a diagnostic profile. Incomplete and/or inaccurate profiles contribute to misdiagnosis.</p> <p>Methods</p> <p>In order to create more complete tissue structure profiles, we adapted our cell-graph method for extracting quantitative features from histopathology images to now capture temporospatial traits of three-dimensional collagen hydrogel cell cultures. Cell-graphs were proposed to characterize the spatial organization between the cells in tissues by exploiting graph theory wherein the nuclei of the cells constitute the <it>nodes </it>and the approximate adjacency of cells are represented with <it>edges</it>. We chose 11 different cell types representing non-tumorigenic, pre-cancerous, and malignant states from multiple tissue origins.</p> <p>Results</p> <p>We built cell-graphs from the cellular hydrogel images and computed a large set of features describing the structural characteristics captured by the graphs over time. Using three-mode tensor analysis, we identified the five most significant features (metrics) that capture the compactness, clustering, and spatial uniformity of the 3D architectural changes for each cell type throughout the time course. Importantly, four of these metrics are also the discriminative features for our histopathology data from our previous studies.</p> <p>Conclusions</p> <p>Together, these descriptive metrics provide rigorous quantitative representations of image information that other image analysis methods do not. Examining the changes in these five metrics allowed us to easily discriminate between all 11 cell types, whereas differences from visual examination of the images are not as apparent. These results demonstrate that application of the cell-graph technique to 3D image data yields discriminative metrics that have the potential to improve the accuracy of image-based tissue profiles, and thus improve the detection and diagnosis of disease.</p

Automatic Tumor-Stroma Separation in Fluorescence TMAs Enables the Quantitative High-Throughput Analysis of Multiple Cancer Biomarkers

The upcoming quantification and automation in biomarker based histological tumor evaluation will require computational methods capable of automatically identifying tumor areas and differentiating them from the stroma. As no single generally applicable tumor biomarker is available, pathology routinely uses morphological criteria as a spatial reference system. We here present and evaluate a method capable of performing the classification in immunofluorescence histological slides solely using a DAPI background stain. Due to the restriction to a single color channel this is inherently challenging. We formed cell graphs based on the topological distribution of the tissue cell nuclei and extracted the corresponding graph features. By using topological, morphological and intensity based features we could systematically quantify and compare the discrimination capability individual features contribute to the overall algorithm. We here show that when classifying fluorescence tissue slides in the DAPI channel, morphological and intensity based features clearly outpace topological ones which have been used exclusively in related previous approaches. We assembled the 15 best features to train a support vector machine based on Keratin stained tumor areas. On a test set of TMAs with 210 cores of triple negative breast cancers our classifier was able to distinguish between tumor and stroma tissue with a total overall accuracy of 88%. Our method yields first results on the discrimination capability of features groups which is essential for an automated tumor diagnostics. Also, it provides an objective spatial reference system for the multiplex analysis of biomarkers in fluorescence immunohistochemistry