Predictive modeling of influenza in New England using a recurrent deep neural network

Predicting seasonal variation in influenza epidemics is an ongoing challenge. To better predict seasonal influenza and provide early warning of pandemics, a novel approach to Influenza-Like-Illness (ILI) prediction was developed. This approach combined a deep neural network with ILI, climate, and population data. A predictive model was created using a deep neural network based on TensorFlow 2.0 Beta. The model used Long-Short Term Memory (LSTM) nodes. Data was collected from the Center for Disease Control, the National Center for Environmental Information (NCEI) and the United States Census Bureau. These parameters were temperature, precipitation, wind speed, population size, vaccination rate and vaccination efficacy. Temperature was confirmed as the greatest predictor for ILI rates, with precipitation providing a small increase in predictive power. After training, the model was able to predict ILI rates 10 weeks out. As a result of this thesis, a framework was developed that may be applied to weekly ILI tracking as well as early prediction of outlier pandemic years

In silico identification of small molecule agonist binding sites on KCC2

Purpose: Potassium-Chloride Cotransporter 2 (KCC2) is a neuronal membrane protein specific to the central nervous system. It is responsible for removing Cl- ions from the intracellular space, maintaining a normal Cl- gradient essential for proper function at inhibitory synapses. Dysregulation causes an upward shift in the Cl- reversal potential resulting in a hyperexcitable state of the postsynaptic neuron. Existing literature indicates that KCC2 may be involved in the addiction pathway of a variety of drugs of abuse, including opioids and alcohol. This makes KCC2 an attractive potential drug target when treating substance use disorders. A novel direct KCC2 agonist, VU0500469, was recently identified experimentally; however, no binding sites were identified or characterized. The goal of this project is to identify likely binding sites of this protein-ligand pair via computer simulation. Methods: A 3D model of human KCC2 was obtained from RCSB Protein Databank. VU0500469 was reconstructed manually. Protein-ligand computational simulations were run using AutoDock Tools and AutoDock Vina, GNINA, and P2Rank to identify direct interactions between VU0500469, and KCC2. Results: Results between simulations were then compared, and several possible VU0500469 binding pocket sites were successfully identified. We plan to further investigate molecular binding dynamics using CHARMM. Conclusion: The binding sites identified may represent targets for the development of additional KCC2 agonists

LSTM-based Recurrent Neural Network Predicts Influenza-like-illness in Variable Climate Zones

Purpose: Influenza virus is responsible for a recurrent, yearly epidemic in most temperate regions of the world. For the 2021-2022 season the CDC reports 5000 deaths and 100,000 hospitalizations, a significant number despite the confounding presence of SARS-CoV-2. The mechanisms behind seasonal variance in flu burden are not well understood. Based on a previously validated model, this study seeks to expand understanding of the impact of variable climate regions on seasonal flu trends. To that end, three climate regions have been selected. Each region represents a different ecological region and provides different weather patterns showing how the climate variables impact flu transmission in different regions. Methods: An LSTM-Based recurrent neural network was used to predict influenza-like-illness trends for three separate locations: Hawaii, Vermont, and Nevada. Flu data were gathered from the CDC as weekly influenza-like-illness (ILI) percents. Weather data were collected from Visual Crossing and included temperature, UV index, solar radiation, precipitation, and humidity. These weather data sets were chosen based on previous work results and a literature search. Data were prepared and the model trained as described previously. Results: All three regions showed strong seasonality of flu trends with Hawaii having the largest absolute ILI values. Temperature showed a moderate negative correlation with ILI in all three regions (Vermont = -54, Nevada = -0.56, Hawaii = -0.44). Humidity was moderately correlated in Nevada (0.47) and weakly correlated with ILI in Hawaii (0.22). Vermont ILI did not correlate with humidity. Precipitation and wind speed were weakly correlated in all three regions. Solar radiation and UV index showed moderate correlation in Vermont (-0.33, -0.36) and Nevada (-0.5263, -0.55) however only weak correlation in Hawaii (-0.15, -0.18). When trained on the complete data set model performance at +1 week was comparable to the previously validated model. Conclusions: Preliminary results indicate that temperature is a moderate predictor of ILI rates. Additionally, humidity, solar radiation, and UV index present promising prediction variables. Initial modeling attempts revealed acceptable performance in all regions. While seasonality appeared similar in each region, differences in correlation with weather variables may reveal variability in the driving forces behind ILI rates

The role of the KCC2 in substance use and abuse: A systematic review [Protocol]

This poster presents the protocol for an ongoing systematic review investigating the role of potassium chloride co-transporter 2 (KCC2) in substance use, abuse, and addiction

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