Fe-rich X-ray amorphous material records past climate and persistence of water on Mars.

X-ray amorphous material comprises 15-73 wt.% of sedimentary rocks and eolian sediments in Gale crater. This material is variably siliceous and iron rich but aluminum poor. The presence of volatiles is consistent with the existence of incipient weathering products. To better understand the implications of this material for past aqueous conditions on Mars, here we investigate X-ray amorphous material formation and longevity within terrestrial iron rich soils with varying ages and environmental conditions using bulk and selective dissolution methods, X-ray diffraction, and transmission electron microscopy. Results indicate that in situ aqueous alteration is required to concentrate iron into clay-size fraction material. Cooler climates promote the formation and persistence of X-ray amorphous material whereas warmer climates promote the formation of crystalline secondary phases. Iron rich X-ray amorphous material formation and persistence on Mars are therefore consistent with past cool and relatively wet environments followed by long-term cold and dry conditions.

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.

Social Listening as a Rapid Approach to Collecting and Analyzing COVID-19 Symptoms and Disease Natural Histories Reported by Large Numbers of Individuals.

Given the severe and rapid impact of COVID-19, the pace of information sharing has been accelerated. However, traditional methods of disseminating and digesting medical information can be time-consuming and cumbersome. In a pilot study, the authors used social listening to quickly extract information from social media channels to explore what people with COVID-19 are talking about regarding symptoms and disease progression. The goal was to determine whether, by amplifying patient voices, new information could be identified that might have been missed through other sources. Two data sets from social media groups of people with or presumed to have COVID-19 were analyzed: a Facebook group poll, and conversation data from a Reddit group including detailed disease natural history-like posts. Content analysis and a customized analytics engine that incorporates machine learning and natural language processing were used to quickly identify symptoms mentioned. Key findings include more than 20 symptoms in the data sets that were not listed in online lists of symptoms from 4 respected medical information sources. The disease natural history-like posts revealed that people can experience symptoms for many weeks and that some symptoms change over time. This study demonstrates that social media can offer novel insights into patient experiences as a source of real-world data. This inductive research approach can quickly generate descriptive information that can be used to develop hypotheses and new research questions. Also, the method allows rapid assessments of large numbers of social media conversations that could be applied to monitor public health for emerging and rapidly spreading diseases such as COVID-19.