Exploring Glucose Dysregulation in Migraine: Insights from Continuous Glucose Monitoring

Despite being associated with hypoglycemia for nearly a century, a definitive relationship between migraines and glucose dysregulation remains elusive. Accumulating evidence suggests that migraines are in part due to a metabolic mismatch between cerebral demand and available energy. Research analyzing plasma glucose levels and migraine activity may further elucidate this interface between metabolic dysregulation and migraine pathophysiology and may potentially open avenues for therapeutic interventions targeting holistic metabolism for migraine management

Development of a Protocol for Obtaining Biological Samples for Genetic Testing from Remote Individuals

Pharmacogenomic sequencing allows individuals to learn more about how they will respond to certain medications but requires shipping of a biological sample. One complication of sending biological samples to remote laboratories is stability. Blood generally yields sufficient quantities of high-quality DNA but requires a clinic visit. Saliva and buccal swabs are routinely used for DNA extractions, but the DNA quality is notoriously low due to the presence of bacteria in the mouth. Additionally, elderly individuals have difficulty producing enough saliva for testing, and the tubes contain several milliliters of liquid and shipping requires special considerations. Dried blood spot cards, which serve as an alternative to saliva and buccal swabs, yield high-quality DNA and ship easily, but may produce a lower yield. This project aims to determine which biological sample methods can reasonably be obtained from remote individuals

LSTM-based recurrent neural network provides effective short term flu forecasting

Abstract Background Influenza virus is responsible for a yearly epidemic in much of the world. To better predict short-term, seasonal variations in flu infection rates and possible mechanisms of yearly infection variation, we trained a Long Short-Term Memory (LSTM)-based deep neural network on historical Influenza-Like-Illness (ILI), climate, and population data. Methods Data were collected from the Centers for Disease Control and Prevention (CDC), the National Center for Environmental Information (NCEI), and the United States Census Bureau. The model was initially built in Python using the Keras API and tuned manually. We explored the roles of temperature, precipitation, local wind speed, population size, vaccination rate, and vaccination efficacy. The model was validated using K-fold cross validation as well as forward chaining cross validation and compared to several standard algorithms. Finally, simulation data was generated in R and used for further exploration of the model. Results We found that temperature is the strongest predictor of ILI rates, but also found that precipitation increased the predictive power of the network. Additionally, the proposed model achieved a +1 week prediction mean absolute error (MAE) of 0.1973. This is less than half of the MAE achieved by the next best performing algorithm. Additionally, the model accurately predicted simulation data. To test the role of temperature in the network, we phase-shifted temperature in time and found a predictable reduction in prediction accuracy. Conclusions The results of this study suggest that short term flu forecasting may be effectively accomplished using architectures traditionally reserved for time series analysis. The proposed LSTM-based model was able to outperform comparison models at the +1 week time point. Additionally, this model provided insight into the week-to-week effects of climatic and biotic factors and revealed potential patterns in data series. Specifically, we found that temperature is the strongest predictor of seasonal flu infection rates. This information may prove to be especially important for flu forecasting given the uncertain long-term impact of the SARS-CoV-2 pandemic on seasonal influenza