No abstract

PurposeAMD is the leading cause of irreversible blindness in older individuals in the Western world, and there are currently no therapies to halt disease progression. Studies suggest that the commonly prescribed antidiabetic drug, metformin, is associated with decreased risk of several ocular diseases, but no work has investigated the effect of metformin use on development of AMD. Thus, we aim to investigate whether metformin use is associated with decreased risk of developing AMD.MethodsIn this retrospective case-control study, we used medical records from patients older than 55 who have visited a University of Florida health clinic. Three controls were matched for every AMD case, defined by International Classification of Diseases, Ninth Revision code, based on the Charlson Comorbidity Index to ensure comparable baseline overall health status. Univariate and conditional multivariable logistic regressions were used to determine the association between a variety of covariates, including metformin use, and AMD diagnosis.ResultsMetformin use was associated with decreased odds of developing AMD, independently of the other covariates investigated, with an odds ratio of 0.58 and a 95% confidence interval of 0.43 to 0.79. Other medications assessed were not associated with decreased odds of developing AMD.ConclusionsPatients who had taken metformin had decreased odds of developing AMD, suggesting that metformin may have a therapeutic role in AMD development or progression in those who are at risk. Further work should include clinical trials to investigate prospectively whether metformin has a protective effect in those at risk for developing AMD.

Social media analysis has shown tremendous potential to understand public's opinion on a wide variety of topics. In this paper, we have mined Twitter to understand the public's perception of the Internet of Things (IoT). We first generated the discussion trends of the IoT from multiple Twitter data sources and validated these trends with Google Trends. We then performed sentiment analysis to gain insights of the public’s attitude towards the IoT. As anticipated, our analysis indicates that the public's perception of the IoT is predominantly positive. Further, through topic modeling, we learned that public tweets discussing the IoT were often focused on business and technology. However, the public has great concerns about privacy and security issues toward the IoT based on the frequent appearance of related terms. Nevertheless, no unexpected perceptions were identified through our analysis. Our analysis was challenged by the limited fraction of tweets relevant to our study. Also, the user demographics of Twitter users may not be strongly representative of the population of the general public.

This study examined the AEs most commonly associated with varying classes of painkillers by mining the FAERS database. Our results and methods are relevant for future secondary analyses of big data and for understanding adverse outcomes related to painkillers.