Twitter may be a viable tool for real-time monitoring of suicide risk factors on a large scale. This study demonstrates that individuals who are at risk for suicide may be detected through social media.

BackgroundAdderall is the most commonly abused prescription stimulant among college students. Social media provides a real-time avenue for monitoring public health, specifically for this population.ObjectiveThis study explores discussion of Adderall on Twitter to identify variations in volume around college exam periods, differences across sets of colleges and universities, and commonly mentioned side effects and co-ingested substances.MethodsPublic-facing Twitter status messages containing the term “Adderall” were monitored from November 2011 to May 2012. Tweets were examined for mention of side effects and other commonly abused substances. Tweets from likely students containing GPS data were identified with clusters of nearby colleges and universities for regional comparison.Results213,633 tweets from 132,099 unique user accounts mentioned “Adderall.” The number of Adderall tweets peaked during traditional college and university final exam periods. Rates of Adderall tweeters were highest among college and university clusters in the northeast and south regions of the United States. 27,473 (12.9%) mentioned an alternative motive (eg, study aid) in the same tweet. The most common substances mentioned with Adderall were alcohol (4.8%) and stimulants (4.7%), and the most common side effects were sleep deprivation (5.0%) and loss of appetite (2.6%).ConclusionsTwitter posts confirm the use of Adderall as a study aid among college students. Adderall discussions through social media such as Twitter may contribute to normative behavior regarding its abuse.

BackgroundOne of the leading causes of death in the United States (US) is suicide and new methods of assessment are needed to track its risk in real time.ObjectiveOur objective is to validate the use of machine learning algorithms for Twitter data against empirically validated measures of suicidality in the US population.MethodsUsing a machine learning algorithm, the Twitter feeds of 135 Mechanical Turk (MTurk) participants were compared with validated, self-report measures of suicide risk.ResultsOur findings show that people who are at high suicidal risk can be easily differentiated from those who are not by machine learning algorithms, which accurately identify the clinically significant suicidal rate in 92% of cases (sensitivity: 53%, specificity: 97%, positive predictive value: 75%, negative predictive value: 93%).ConclusionsMachine learning algorithms are efficient in differentiating people who are at a suicidal risk from those who are not. Evidence for suicidality can be measured in nonclinical populations using social media data.

Highlights d OM proteomics show that DcaP is the most abundant channel during rodent infection d The X-ray crystal structure of DcaP reveals a trimeric, porinlike structure d Dicarboxylic acids are transport substrates as shown by electrophysiology d Sulbactam, a clinically relevant b-lactamase inhibitor, permeates through DcaP

BackgroundThe use of social media by health care organizations is growing and provides Web-based tools to connect patients, caregivers, and providers.ObjectiveThe aim was to determine the use and factors predicting the use of social media for health care–related purposes among medically underserved primary care patients.MethodsA cross-sectional survey was administered to 444 patients of a federally qualified community health center.ResultsCommunity health center patients preferred that their providers use email, cell phones for texting, and Facebook and cell phone apps for sharing health information. Significantly more Hispanic than white patients believed their providers should use Facebook (P=.001), YouTube (P=.01), and Twitter (P=.04) for sharing health information. Use and intentions to use social media for health-related purposes were significantly higher for those patients with higher subjective norm scores.ConclusionsUnderstanding use and factors predicting use can increase adoption and utilization of social media for health care–related purposes among underserved patients in community health centers.