Novel Sulfated Polysaccharides Disrupt Cathelicidins, Inhibit RAGE and Reduce Cutaneous Inflammation in a Mouse Model of Rosacea

Rosacea is a common disfiguring skin disease of primarily Caucasians characterized by central erythema of the face, with telangiectatic blood vessels, papules and pustules, and can produce skin thickening, especially on the nose of men, creating rhinophyma. Rosacea can also produce dry, itchy eyes with irritation of the lids, keratitis and corneal scarring. The cause of rosacea has been proposed as over-production of the cationic cathelicidin peptide LL-37.We tested a new class of non-anticoagulant sulfated anionic polysaccharides, semi-synthetic glycosaminoglycan ethers (SAGEs) on key elements of the pathogenic pathway leading to rosacea. SAGEs were anti-inflammatory at ng/ml, including inhibition of polymorphonuclear leukocyte (PMN) proteases, P-selectin, and interaction of the receptor for advanced glycation end-products (RAGE) with four representative ligands. SAGEs bound LL-37 and inhibited interleukin-8 production induced by LL-37 in cultured human keratinocytes. When mixed with LL-37 before injection, SAGEs prevented the erythema and PMN infiltration produced by direct intradermal injection of LL-37 into mouse skin. Topical application of a 1% (w/w) SAGE emollient to overlying injected skin also reduced erythema and PMN infiltration from intradermal LL-37.Anionic polysaccharides, exemplified by SAGEs, offer potential as novel mechanism-based therapies for rosacea and by extension other LL-37-mediated and RAGE-ligand driven skin diseases

Automated Classification of Written Proficiency Levels on the CEFR-Scale through Complexity Contours and RNNs

Automatically predicting the level of second language (L2) learner proficiency is an emerging topic of interest and research based on machine learning approaches to language learning and development. The key to the present paper is the combined use of what we refer to as ‘complexity contours’, a series of measurements of indices of L2 proficiency obtained by a computational tool that implements a sliding window technique, and recurrent neural network (RNN) classifiers that adequately capture the sequential information in those contours. We used the EF-Cambridge Open Language Database (Geertzen et al. 2013) with its labelled Common European Framework of Reference (CEFR) levels (Council of Europe 2018) to predict six classes of L2 proficiency levels (A1, A2, B1, B2, C1, C2) in the assessment of writing skills. Our experiments demonstrate that an RNN classifier trained on complexity contours achieves higher classification accuracy than one trained on text-average complexity scores. In a secondary experiment, we determined the relative importance of features from four distinct categories through a sensitivity-based pruning technique. Our approach makes an important contribution to the field of automated identification of language proficiency levels, more specifically, to the increasing efforts towards the empirical validation of CEFR levels

Automated Classification of Written Proficiency Levels on the CEFR-Scale through Complexity Contours and RNNs

Automatically predicting the level of second language (L2) learner proficiency is an emerging topic of interest and research based on machine learning approaches to language learning and development. The key to the present paper is the combined use of what we refer to as ‘complexity contours’, a series of measurements of indices of L2 proficiency obtained by a computational tool that implements a sliding window technique, and recurrent neural network (RNN) classifiers that adequately capture the sequential information in those contours. We used the EF-Cambridge Open Language Database (Geertzen et al. 2013) with its labelled Common European Framework of Reference (CEFR) levels (Council of Europe 2018) to predict six classes of L2 proficiency levels (A1, A2, B1, B2, C1, C2) in the assessment of writing skills. Our experiments demonstrate that an RNN classifier trained on complexity contours achieves higher classification accuracy than one trained on text-average complexity scores. In a secondary experiment, we determined the relative importance of features from four distinct categories through a sensitivity-based pruning technique. Our approach makes an important contribution to the field of automated identification of language proficiency levels, more specifically, to the increasing efforts towards the empirical validation of CEFR levels

Modelling dermal drug distribution after topical application in human

Purpose:To model and interpret drug distribution in the dermis and underlying tissues after topical application which is relevant to the treatment of local conditions. Methods:We created a new physiological pharmacokinetic model to describe the effect of blood flow, blood protein binding and dermal binding on the rate and depth of penetration of topical drugs into the underlying skin. We used this model to interpret literature in vivo human biopsy data on dermal drug concentration at various depths in the dermis after topical application of six substances. This interpretation was facilitated by our in vitro human dermal penetration studies in which dermal diffusion coefficient and binding were estimated. Results: The model shows that dermal diffusion alone cannot explain the in vivo data, and blood and/or lymphatic transport to deep tissues must be present for almost all of the drugs tested. Conclusion:Topical drug delivery systems for deeper tissue delivery should recognise that blood/lymphatic transport may dominate over dermal diffusion for certain compounds