Cortical bone allografts suffer from high rates of failure due to poor integration with host tissue, leading to non-union, fracture, and infection following secondary procedures. Here, we report a method for modifying the surfaces of cortical bone with coatings that have biological functions that may help overcome these challenges. These chitosan-heparin coatings promote mesenchymal stem cell attachment and have significant antibacterial activity against both S. aureus and E. coli. Furthermore, their chemistry is similar to coatings we have reported on previously, which effectively stabilize and deliver heparin-binding growth factors. These coatings have potential as synthetic periosteum for improving bone allograft outcomes.

The identification of drug-drug interactions (DDIs) is important for patient safety; yet, compared to other pharmacovigilance work, a limited amount of research has been conducted in this space. Recent work has successfully applied a method of deriving distributed vector representations from structured biomedical knowledge, known as Embedding of Semantic Predications (ESP), to the problem of predicting individual drug side effects. In the current paper we extend this work by applying ESP to the problem of predicting polypharmacy side-effects for particular drug combinations, building on a recent reconceptualization of this problem as a network of drug nodes connected by side effect edges. We evaluate ESP embeddings derived from the resulting graph on a side-effect prediction task against a previously reported graph convolutional neural network approach, using the same data and evaluation methods. We demonstrate that ESP models perform better, while being faster to train, more re-usable, and significantly simpler.

Our methods can assist the pharmacovigilance process using information from the biomedical literature. Unsupervised pretraining generates a rich relationship-based representational foundation for machine learning techniques to classify drugs in the context of a putative side effect, given known examples.

Adverse event report (AER) data are a key source of signal for post marketing drug surveillance. The standard methodology to analyze AER data applies disproportionality metrics, which estimate the strength of drug/side-effect associations from discrete counts of their occurrence at report level. However, in other domains, improvements in predictive modeling accuracy have been obtained through representation learning, where discrete features are replaced by distributed representations learned from unlabeled data. This paper describes aer2vec, a novel representational approach for AER data in which concept embeddings emerge from neural networks trained to predict drug/side-effect co-occurrence. Trained models are evaluated for their utility in identifying drug/side-effect relationships, with improvements over disproportionality metrics in most cases. In addition, we evaluate the utility of an otherwise-untapped resource in the Food and Drug Administration (FDA) AER system -reporter designations of suspected causality -and find that incorporating this information enhances performance of all models evaluated.

The presence of opioid receptors has been confirmed by a variety of techniques in vertebrate retinas including those of mammals; however, in most reports the location of these receptors has been limited to retinal regions rather than specific cell-types. Concurrently, our knowledge of the physiological functions of opioid signaling in the retina is based on only a handful of studies. To date, the best documented opioid effect is the modulation of retinal dopamine release, which has been shown in a variety of vertebrate species. Nonetheless, it is not known if opioids can affect dopaminergic amacrine cells (DACs) directly, via opioid receptors expressed by DACs. This study, using immunohistochemical methods, sought to determine whether (1) μ- and δ-opioid receptors (MORs and DORs, respectively) are present in the mouse retina, and if present, (2) are they expressed by DACs. We found that MOR and DOR immunolabeling was associated with multiple cell-types in the inner retina, suggesting that opioids might influence visual information processing at multiple sites within the mammalian retinal circuitry. Specifically, colabeling studies with the DAC molecular marker anti-tyrosine hydroxylase antibody showed that both MOR and DOR immunolabeling localize to DACs. These findings predict that opioids can affect DACs in the mouse retina directly, via MOR and DOR signaling, and might modulate dopamine release as reported in other mammalian and non-mammalian retinas.

Introduction: On account of well-documented limitations of data collected by spontaneous reporting systems (SRS), such as bias and under-reporting, a number of authors have evaluated the utility of other data sources for the purpose of pharmacovigilance, including the biomedical literature. Previous work has demonstrated the utility of literature-derived distributed representations (concept embeddings) with machine learning for the purpose of drug side-effect prediction. In terms of data sources, these methods are complementary, observing drug safety from two different perspectives (knowledge extracted from the literature and statistics from SRS data). However, the combined utility of these pharmacovigilance methods has yet to be evaluated.Objective: This research investigates the utility of directly or indirectly combining observational signal from SRS with literature-derived distributed representations into a single feature vector or in an ensemble approach for downstream machine learning (logistic regression).Methods: Leveraging a recently developed representation scheme, concept embeddings were generated from relational connections extracted from the literature and composed to represent drug and associated adverse reactions, as defined by two reference standards of positive (likely causal) and negative (no causal evidence) pairs. Embeddings were presented with and without common measures of observational signal from SRS sources to logistic regressors, and performance was evaluated with the Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) metric.Results: ROC AUC performance with these composite models improves up to ≈20% over SRSbased disproportionality metrics alone and exceeds the best prior results reported in the literature when models leverage both sources of information. Conclusions:Results from this study support the hypothesis that knowledge extracted from the literature can enhance the performance of SRS-based methods (and vice versa). Across reference sets, using literature and SRS information together performed better than using either source

Stress has, for many years, been linked to the onset of autoimmune disease and, in particular, autoimmune thyroid disease (AITD). Whilst the exact mechanism of this association is unknown, it is clear that episodes of stress can induce profound changes in the immune system. More specifically, recent studies from several laboratories have shown an association between the expression of stress proteins and, particularly, the Hsp70 family with AITD. Our own studies describe a thyroid-specific Hsp70 which shares antigenicity with the key thyroid autoantigen, thyroid peroxidase. Further studies on the molecular basis for this observation are, however, hampered by the lack of a suitably validated thyroid cell model. In this paper we compare the response of primary cultures of human thyrocytes to hyperthermia with the response seen in the immortalized human thyroid cell line HTori3. Both cell types responded in a broadly similar manner, synthesizing proteins from two of the major stress protein families, Hsp70 and Hsp90. In the primary human thyrocyte cultures the 70 kDa proteins showed a 7.5-fold increase and the 90 kDa proteins a 2.7-fold increase with hyperthermia whilst in the HTori3 cells the increases in response to hyperthermia were 10- and 6.5-fold, respectively. We also show a dose-dependent stress response in HTori3 cells cultured in the presence of arsenite ions. We conclude that the response of this highly differentiated and stable thyroid cell line to stress is similar to that seen in primary cultures of human thyroid cells and that these immortalized cells will afford a convenient and effective model for the further study of the role of stress in the pathology of AITD.

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