Muscle relaxant induced pancreatitis leading to hyperosmolar hyperglycemic state.

Muscle relaxants are commonly prescribed in the United States but may have deleterious side effects that are unrecognized by physicians. Here, we report a 55-year-old Caucasian man who developed pancreatitis and a subsequent hyperosmolar hyperglycemic state after being prescribed tizanidine. The patient had untreated hypertriglyceridemia, unbeknownst to the prescribing physician. While hypertriglyceridemia is a widely understood risk factor for pancreatitis, its incidence with tizanidine is not. As an alpha-2 agonist, tizanidine slows gastrointestinal motility by inhibiting gastrointestinal smooth muscle contraction, which could lead to ileus which occurred in this patient. Alpha-2 agonists further contract the hepato-pancreatic sphincter, which may result in obstruction of pancreatic enzyme flow via the pancreatic duct. This patient's case of pancreatitis was precipitated by 2 factors: (i) his use of tizanidine and (ii) hypertriglyceridemia. This case demonstrates that patients presenting with severe hypertriglyceridemia, or other potential risk factors for pancreatitis, should not be prescribed tizanidine.

How the medium shapes the message: Printing and the rise of the arts and sciences.

Communication technologies, from printing to social media, affect our historical records by changing the way ideas are spread and recorded. Yet, finding statistical evidence of this fact has been challenging. Here we combine a common causal inference technique (instrumental variable estimation) with a dataset on nearly forty thousand biographies from Wikipedia (Pantheon 2.0), to study the effect of the introduction of printing in European cities on Wikipedia's digital biographical records. By using a city's distance to Mainz as an instrument for the adoption of the movable type press, we show that European cities that adopted printing earlier were more likely to become the birthplace of a famous scientist or artist during the years following the invention of printing. We bring these findings to recent communication technologies by showing that the number of radios and televisions in a country correlates with the number of globally famous performing artists and sports players born in that country, even after controlling for GDP, population, and including country and year fixed effects. These findings support the hypothesis that the introduction of communication technologies can bias historical records in the direction of the content that is best suited for each technology.

Prediction of Happy-Sad mood from daily behaviors and previous sleep history.

We collected and analyzed subjective and objective data using surveys and wearable sensors worn day and night from 68 participants for ~30 days each, to address questions related to the relationships among sleep duration, sleep irregularity, self-reported Happy-Sad mood and other daily behavioral factors in college students. We analyzed this behavioral and physiological data to (i) identify factors that classified the participants into Happy-Sad mood using support vector machines (SVMs); and (ii) analyze how accurately sleep duration and sleep regularity for the past 1-5 days classified morning Happy-Sad mood. We found statistically significant associations amongst Sad mood and poor health-related factors. Behavioral factors including the frequency of negative social interactions, and negative emails, and total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity and sleep duration predicted daily Happy-Sad mood with 65-80% accuracy. The number of nights giving the best prediction of Happy-Sad mood varied for different individuals.

Recognizing Academic Performance, Sleep Quality, Stress Level, and Mental Health using Personality Traits, Wearable Sensors and Mobile Phones.

What can wearable sensors and usage of smart phones tell us about academic performance, self-reported sleep quality, stress and mental health condition? To answer this question, we collected extensive subjective and objective data using mobile phones, surveys, and wearable sensors worn day and night from 66 participants, for 30 days each, totaling 1,980 days of data. We analyzed daily and monthly behavioral and physiological patterns and identified factors that affect academic performance (GPA), Pittsburg Sleep Quality Index (PSQI) score, perceived stress scale (PSS), and mental health composite score (MCS) from SF-12, using these month-long data. We also examined how accurately the collected data classified the participants into groups of high/low GPA, good/poor sleep quality, high/low self-reported stress, high/low MCS using feature selection and machine learning techniques. We found associations among PSQI, PSS, MCS, and GPA and personality types. Classification accuracies using the objective data from wearable sensors and mobile phones ranged from 67-92%.