Characterizing the Discussion of Antibiotics in the Twittersphere: What is the Bigger Picture?

Background

User content posted through Twitter has been used for biosurveillance, to characterize public perception of health-related topics, and as a means of distributing information to the general public. Most of the existing work surrounding Twitter and health care has shown Twitter to be an effective medium for these problems but more could be done to provide finer and more efficient access to all pertinent data. Given the diversity of user-generated content, small samples or summary presentations of the data arguably omit a large part of the virtual discussion taking place in the Twittersphere. Still, managing, processing, and querying large amounts of Twitter data is not a trivial task. This work describes tools and techniques capable of handling larger sets of Twitter data and demonstrates their use with the issue of antibiotics.

Objective

This work has two principle objectives: (1) to provide an open-source means to efficiently explore all collected tweets and query health-related topics on Twitter, specifically, questions such as what users are saying and how messages are spread, and (2) to characterize the larger discourse taking place on Twitter with respect to antibiotics.

Methods

Open-source software suites Hadoop, Flume, and Hive were used to collect and query a large number of Twitter posts. To classify tweets by topic, a deep network classifier was trained using a limited number of manually classified tweets. The particular machine learning approach used also allowed the use of a large number of unclassified tweets to increase performance.

Results

Query-based analysis of the collected tweets revealed that a large number of users contributed to the online discussion and that a frequent topic mentioned was resistance. A number of prominent events related to antibiotics led to a number of spikes in activity but these were short in duration. The category-based classifier developed was able to correctly classify 70% of manually labeled tweets (using a 10-fold cross validation procedure and 9 classes). The classifier also performed well when evaluated on a per category basis.

Conclusions

Using existing tools such as Hive, Flume, Hadoop, and machine learning techniques, it is possible to construct tools and workflows to collect and query large amounts of Twitter data to characterize the larger discussion taking place on Twitter with respect to a particular health-related topic. Furthermore, using newer machine learning techniques and a limited number of manually labeled tweets, an entire body of collected tweets can be classified to indicate what topics are driving the virtual, online discussion. The resulting classifier can also be used to efficiently explore collected tweets by category and search for messages of interest or exemplary content.     

Early Childhood Gut Microbiomes Show Strong Geographic Differences Among Subjects at High Risk for Type 1 Diabetes

OBJECTIVE

Gut microbiome dysbiosis is associated with numerous diseases, including type 1 diabetes. This pilot study determines how geographical location affects the microbiome of infants at high risk for type 1 diabetes in a population of homogenous HLA class II genotypes.

RESEARCH DESIGN AND METHODS

High-throughput 16S rRNA sequencing was performed on stool samples collected from 90 high-risk, nonautoimmune infants participating in The Environmental Determinants of Diabetes in the Young (TEDDY) study in the U.S., Germany, Sweden, and Finland.

RESULTS

Study site–specific patterns of gut colonization share characteristics across continents. Finland and Colorado have a significantly lower bacterial diversity, while Sweden and Washington state are dominated by Bifidobacterium in early life. Bacterial community diversity over time is significantly different by geographical location.

CONCLUSIONS

The microbiome of high-risk infants is associated with geographical location. Future studies aiming to identify the microbiome disease phenotype need to carefully consider the geographical origin of subjects.     

Aggregation-prone c9FTD/ALS poly(GA) RAN-translated proteins cause neurotoxicity by inducing ER stress

The occurrence of repeat-associated non-ATG (RAN) translation, an atypical form of translation of expanded repeats that results in the synthesis of homopolymeric expansion proteins, is becoming more widely appreciated among microsatellite expansion disorders. Such disorders include amyotrophic lateral sclerosis and frontotemporal dementia caused by a hexanucleotide repeat expansion in the C9ORF72 gene (c9FTD/ALS). We and others have recently shown that this bidirectionally transcribed repeat is RAN translated, and the “c9RAN proteins” thusly produced form neuronal inclusions throughout the central nervous system of c9FTD/ALS patients. Nonetheless, the potential contribution of c9RAN proteins to disease pathogenesis remains poorly understood. In the present study, we demonstrate that poly(GA) c9RAN proteins are neurotoxic and may be implicated in the neurodegenerative processes of c9FTD/ALS. Specifically, we show that expression of poly(GA) proteins in cultured cells and primary neurons leads to the formation of soluble and insoluble high molecular weight species, as well as inclusions composed of filaments similar to those observed in c9FTD/ALS brain tissues. The expression of poly(GA) proteins is accompanied by caspase-3 activation, impaired neurite outgrowth, inhibition of proteasome activity, and evidence of endoplasmic reticulum (ER) stress. Of importance, ER stress inhibitors, salubrinal and TUDCA, provide protection against poly(GA)-induced toxicity. Taken together, our data provide compelling evidence towards establishing RAN translation as a pathogenic mechanism of c9FTD/ALS, and suggest that targeting the ER using small molecules may be a promising therapeutic approach for these devastating diseases.     

Detecting intraannual dietary variability in wild mountain gorillas by stable isotope analysis of feces

We use stable isotope ratios in feces of wild mountain gorillas (Gorilla beringei) to test the hypothesis that diet shifts within a single year, as measured by dry mass intake, can be recovered. Isotopic separation of staple foods indicates that intraannual changes in the isotopic composition of feces reflect shifts in diet. Fruits are isotopically distinct compared with other staple foods, and peaks in fecal δ¹³C values are interpreted as periods of increased fruit feeding. Bayesian mixing model results demonstrate that, although the timing of these diet shifts match observational data, the modeled increase in proportional fruit feeding does not capture the full shift. Variation in the isotopic and nutritional composition of gorilla foods is largely independent, highlighting the difficulty for estimating nutritional intake with stable isotopes. Our results demonstrate the potential value of fecal sampling for quantifying short-term, intraindividual dietary variability in primates and other animals with high temporal resolution even when the diet is composed of C₃ plants.