Since the first reports on foam sclerotherapy, multiple studies have been conducted to determine the physical properties and behavior of foams, but relatively little is known about their biological effects on the endothelial cells lining the vessel wall. Moreover, a systematic comparison of the biological performance of foams produced with different methods has not been carried out yet. Herein, a 2D in vitro method was developed to compare efficacy of commercially available polidocanol injectable foam (PEM, Varithena) and physician-compounded foams (PCFs). Endothelial cell attachment upon treatment with foam was quantified as an indicator of therapeutic efficacy, and was correlated with foam physical characteristics and administration conditions. An ex vivo method was also developed to establish the disruption and permeabilisation of the endothelium caused by sclerosing agents. It relied on the quantitation of extravasated bovine serum albumin conjugated to Evans Blue, as an indicator of endothelial permeability. In our series of comparisons, PEM presented a greater overall efficacy compared to PCFs, across the different biological models, which was attributed to its drainage dynamics and gas formulation. This is consistent with earlier studies that indicated superior physical cohesiveness of PEM compared to PCFs.
In Document Image Understanding, one of the fundamental tasks is that of recognizing semantically relevant components in the layout extracted from a document image. This process can be automatized by learning classifiers able to automatically label such components. However, the learning process assumes the availability of a huge set of documents whose layout components have been previously manually labeled. Indeed, this contrasts with the more common situation in which we have only few labeled documents and abundance of unlabeled ones. In addition, labeling layout documents introduces further complexity aspects due to multi-modal nature of the components (textual and spatial information may coexist). In this work, we investigate the application of a relational classifier that works in the transductive setting. The relational setting is justified by the multi-modal nature of the data we are dealing with, while transduction is justified by the possibility of exploiting the large amount of information conveyed in the unlabeled layout components. The classifier bootstraps the labeling process in an iterative way: reliable classifications are used in subsequent iterative steps as training examples. The proposed computational solution has been evaluated on document images of scientific literature
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