Social Listening in Gout: Impact of Proactive vs. Reactive Management on Self-Reported Emotional States.

INTRODUCTION: This study aimed to characterize patient-reported outcomes from social media conversations in the gout community. The impact of management strategy differences on the community's emotional states was explored. METHODS: We analyzed two social media sources using a variety of natural language processing techniques. We isolated conversations with a high probability of discussing disease management (score > 0.99). These conversations were stratified by management type: proactive or reactive. The polarity (positivity/negativity) of language and emotions conveyed in statements shared by community members was assessed by management type. RESULTS: Among the statements related to management, reactive management (e.g., urgent care) was mentioned in 0.5% of statements, and proactive management (e.g., primary care) was mentioned in 0.6% of statements. Reactive management statements had a significantly larger proportion of negative words (59%) than did proactive management statements (44%); "fear" occurred more frequently with reactive statements, whereas "trust" predominated in proactive statements. Allopurinol was the most common medication in proactive management statements, whereas reactive management had significantly higher counts of prednisone/steroid mentions. CONCLUSIONS: A unique aspect of examining gout-related social media conversations is the ability to better understand the intersection of clinical management and emotional impacts in the gout community. The effect of social media statements was significantly stratified by management type for gout community members, where proactive management statements were characterized by more positive language than reactive management statements. These results suggest that proactive disease management may result in more positive mental and emotional experiences in patients with gout.

High Throughput and Computational Repurposing for Neglected Diseases.

PURPOSE: Neglected tropical diseases (NTDs) represent are a heterogeneous group of communicable diseases that are found within the poorest populations of the world. There are 23 NTDs that have been prioritized by the World Health Organization, which are endemic in 149 countries and affect more than 1.4 billion people, costing these developing economies billions of dollars annually. The NTDs result from four different causative pathogens: protozoa, bacteria, helminth and virus. The majority of the diseases lack effective treatments. Therefore, new therapeutics for NTDs are desperately needed. METHODS: We describe various high throughput screening and computational approaches that have been performed in recent years. We have collated the molecules identified in these studies and calculated molecular properties. RESULTS: Numerous global repurposing efforts have yielded some promising compounds for various neglected tropical diseases. These compounds when analyzed as one would expect appear drug-like. Several large datasets are also now in the public domain and this enables machine learning models to be constructed that then facilitate the discovery of new molecules for these pathogens. CONCLUSIONS: In the space of a few years many groups have either performed experimental or computational repurposing high throughput screens against neglected diseases. These have identified compounds which in many cases are already approved drugs. Such approaches perhaps offer a more efficient way to develop treatments which are generally not a focus for global pharmaceutical companies because of the economics or the lack of a viable market. Other diseases could perhaps benefit from these repurposing approaches.

Self-assembly of aromatic amino acids: a molecular dynamics study.

The self assembly processes of aromatic amino acids, phenylalanine, tyrosine, and tryptophan have been simulated and were observed to form fibril-like aggregates linked to certain rare diseases and instances of biological membrane disruption. Pure systems and their mixtures were studied systematically at constant temperatures and free energy landscapes were produced describing the height and the number of assembled monomers associated with lower energy structures. Consistent with some previous work, aromatic amino acid monomers display a tendency to arrange with a four-fold symmetry. The occurrence of this and other ordered structures increases at higher temperatures. At lower temperatures our binary mixture simulations indicate that increasing tryptophan content drives the assembly process away from the formation of distinct nanostructures and toward disordered aggregates which is in line with experimental observations of pure tryptophan solutions. This work provides molecular level insight to a variety of different physical phenomena relevant to fields including human disease.