Perkins Marquez (hookframe7)

nd open source web-based software application. (The views expressed are those of the authors and do not necessarily represent the views or policies of the US EPA).Ophthalmic pathology has a long tradition in Germany. And, like in general pathology, there is continuous progress due to new technologies and the improvement of molecular biology techniques. Ophthalmic pathology cannot be disregarded, particularly in the context of basic research but also as a medium for understanding pathophysiologic interrelationships and evaluating innovative surgical techniques. By means of various examples, the "four columns" of ophthalmic pathology shall be illustrated diagnostics, clinicopathologic correlation, evaluation of new surgical and medical techniques and finally research. Ophthalmic pathology is not a discipline of the past but is rather one of the future. It develops and improves together with medical and ophthalmological progress and serves, at the same time, as a critical evaluation tool. Clinicopathologic correlations are of paramount importance for a lasting quality in ophthalmology, and we should not risk depriving ourselves of this instrument by carelessly saving at the wrong end and closing our laboratories. Ophthalmic pathology was, is and will further be the gold standard in many aspects of ophthalmology.Developing methods for anticipating the emergence or reemergence of infectious diseases is both important and timely; however, traditional model-based approaches are stymied by uncertainty surrounding the underlying drivers. Here, we demonstrate an operational, mechanism-agnostic detection algorithm for disease (re-)emergence based on early warning signals (EWSs) derived from the theory of critical slowing down. Specifically, we used computer simulations to train a supervised learning algorithm to detect the dynamical footprints of (re-)emergence present in epidemiological data. Our algorithm was then challenged to forecast the slowly manifesting, spatially replicated reemergence of mumps in England in the mid-2000s and pertussis post-1980 in the United States. Our method successfully anticipated mumps reemergence 4 years in advance, during which time mitigation efforts could have been implemented. From 1980 onwards, our model identified resurgent states with increasing accuracy, leading to reliable classification starting in 1992. Additionally, we successfully applied the detection algorithm to 2 vector-transmitted case studies, namely, outbreaks of dengue serotypes in Puerto Rico and a rapidly unfolding outbreak of plague in 2017 in Madagascar. Taken together, these findings illustrate the power of theoretically informed machine learning techniques to develop early warning systems for the (re-)emergence of infectious diseases.Introduction and objective Interleukin (IL)-32 is a pro-inflammatory cytokine not previously studied in relation to abdominal aortic aneurysm (AAA). The aim of this study was to elucidate the expression and localization of IL-32 in AAA. Methods Expression and localization of IL-32 in human aortic tissue was studied with immunohistochemical analysis and Western blot (AAA n = 5; controls n = 4). ELISA was used to measure IL-32 in human plasma samples (AAA n = 140; controls n = 37) and in media from cultured peripheral blood mononuclear cells (PBMCs) from 3 healthy donors. IL-32 mRNA in PBMCs, endothelial cells, aortic smooth muscle cells (SMCs), and aortic tissue samples of AAA (n = 16) and control aortas (n = 9) was measured with qPCR. Results IL-32 was predominantly expressed in SMCs and T-cell-rich areas. Highest mRNA expression was observed in the intima/media layer of the AAA. A weaker protein expression was detected in non-aneurysmal aortas. Expression of IL-32 was confirmed in isolated T cells, macrophages, endothelial cells, and SMCs, where expression was also inducible by cytokines such as interferon-γ. There was no difference in IL-32 expression in plasma between patients and controls. Con