Social Chair, Physiology Student Organization
NIH National Research Service Award (F31)
Sudden cardiac death (SCD) from ventricular arrhythmias claims more lives every year than cancer, diabetes, and all other pathological diseases combined. This is chiefly due to a lack of understanding how SCD manifests (i.e. the underlying mechanisms are unclear), preventing development of newer, more effective therapies. An additional problem is the lack of suitable animal models which mimic the disease as in their human counterparts. The DeMazumder lab has developed a novel guinea pig model of SCD, which allows us to address previously unanswered questions surrounding the mechanisms of sudden death.
People with heart failure (HF) are at the greatest risk of SCD. More than half of the deaths in HF patients are the result of SCD. Currently, there are no suitable treatments to prevent or cure SCD and HF. Therapies in use are palliative and only serve to address symptoms and do not reverse disease progression. Chronic vagus nerve stimulation (VNS), commonly used for treatment in various neurological disorders (e.g. epilepsy), is being clinically evaluated as an exciting new therapy for HF and has been shown to improve ventricular function in HF patients. However, results are conflicting across trials. This is most likely attributed to differences in the empirically selected stimulation parameters and durations. Though chronic VNS is currently being studied as a potential HF therapy, chronic VNS has not yet been studied in the context of SCD. Previous studies conducted by the DeMazumder lab show that activation of the parasympathetic nervous pathway improves cardiac function in HF and reduces SCD incidence. While the effects of acute VNS are well documented, not much is known about the effects of chronic VNS. Studies conducted over 150 years ago by Einbrodt show that acute VNS is capable of abolishing arrhythmia; however, chronic VNS may increase SCD risk by modulating cardiac electrical activity, which is already impacted in HF. However, preliminary data from our lab show that although chronic VNS does impact the heart's electrical properties, it serves to stabilize the heart's natural rhythm, decreasing risk of arrhythmia and abolishing SCD completely in our animal models. Our lab's current objective is to elucidate the underlying pathways and mechanisms linking SCD, HF, and chronic VNS. In doing so, we hope to: (1) improve upon current usage of VNS in HF; (2) apply chronic VNS as a suitable therapy for SCD; and (3) design new therapies that target these understudied pathways of SCD and HF.
My project investigates autonomic sensitivity and response across several animal models incorporating SCD, HF, and chronic VNS. Autonomic dysfunction and remodeling are hallmarks of both SCD and HF. Changes in the way the body responds to natural stressors over time leads to alterations in autonomic nervous signaling cascades and cardiac response. These changes impact how the heart functions (e.g. decreasing the heart's ability to pump blood and oxygenate the body effectively, which in turn increases the heart's workload). By studying how the heart's electrophysiological properties differ in SCD and HF as compared to normal, healthy hearts, we are able to evaluate which steps of the underlying mechanisms to target for development of future therapies for the prevention of SCD.
I am primarily interested in Quantitative Systems Pharmacology (QSP) modeling and am currently looking into blood coagulation, hemophilia, and treatments. This modeling is primarily done with systems of differential equations, and I am most proficient at doing it in Python, though MATLAB or other languages can be used. Currently I am rotating in biomedical informatics, hopefully gaining more experience with deep learning or statistical programming techniques.
While completing my Masters degree in Biomathematics at Ohio State University, I had the opportunity to take many interesting classes. One of these was Biomedical Informatics, taught by Dr. Kevin Coombes. In this course, I was really fascinated with the use of various data analysis techniques on biological data, such as Hierarchical Agglomerative Clustering and K-Means Clustering. In class, we used Hierarchical Clustering and Principal Component Analysis on RNA-Seq data of patients with prostate cancer. The data analysis showed that the data set was skewed and confounded by the chip type used. This result was not evident in the initial analysis of the data. Such analysis techniques have been essential in my Masters thesis project on sleep inertia in children. A research paper by Van Dongen et al.  finds that sleep-deprived patients can be divided into groups. These groups consist of patients who are found to be resilient, vulnerable, or somewhere in between to sleep deprivation. My Masters thesis advisor, Dr. Best, and I used these machine learning algorithms to evaluate data from a 10-minute visual psychomotor vigilance task test on children aged 5 to 12 to find an example of this characteristic. Further, we would like to generate a model which will be able to use baseline reaction times to predict reaction times of patients awakened from deep sleep (as could happen in the case of a fire alarm). As I graduated and moved on from Ohio State, my interest in machine learning and data analysis led me to the join the Miraldi Lab at Cincinnati Children's Hospital as a first-year student in the Systems Biology and Physiology PhD program at UC. In Dr. Miraldi's lab we use bioinformatics tools to help devise mathematical models to predict transcription factor activity, among other things. Dr. Miraldi and her colleagues were able to create the Inferelator in Th17 cells and predict transcription factor activity using sc-RNA-Seq data. A key input to the Inferelator is the prior matrix. This consists of known transcription factor and gene interactions which the program uses to help prune and predict new edges in the network. In my first semester in Dr. Miraldi's lab I helped generate a prior based on ChIP-seq experimental data available from Dr. Matt Weirauch's lab. With these, nearly 24,000 ChIP-seq experiments, we hope that the Inferelator will be able to perform at a much higher level (in regard to precision vs. recall). Even with much smaller data sets, the Inferelator is able to outperform current state-of-the-art techniques, so a larger data set should provide better precsion vs. recall curves. Moving forward, we hope to create a Convolutional Neural Network (CNN) that uses (sc)ATAC-seq data to predict ChIP-seq profiles. We are interested in using (sc)ATAC-seq as it requires significantly fewer cells than ChIP-seq.
 Van Dongen, H.P.A., Baynard, M.D., Maislin, G., and Dinges, D.F., Systematic Interindividual Differences in Neurobehavioral Impairment from Sleep Loss: Evidence of Trait-Like Differential Vulnerability, SLEEP 27.3 (2004), 423-433.
Damage to the spinal cord can be caused in many ways ranging from motor vehicle accidents to recreational activities. Statistics provided by the National Spinal Cord Injury Statistical Center show that, as of 2015, about 12,500 new spinal cord injuries occur each year. According to the National Spinal Cord Injury Association, 8 of every 95 patients with complete spinal cord injuries above C3 die before receiving any medical treatment. The patients who survive are forever dependent on mechanical respirators to breathe. These statistics underscore the importance of research in spinal cord injury. Currently my research focus is in V2a interneurons. V2a are glutamatergic interneurons that are located within the ventral horn of the spinal cord and are found at all spinal levels. These interneurons are shown to be important for locomotion and breathing. A previous study in our lab showed that after C2 hemi-section, increasing the excitability of V2a neurons restored the activity of previously paralyzed diaphragm. This result substantiates the hypothesis that increasing the activity of V2a neurons may help in restoration of function after spinal cord injury. Similarly, another work in our lab revealed that increasing the activity of V2a neurons results in an increase in activity of auxiliary respiratory muscles (ARMs). However, silencing the activity of V2a neurons also leads to an increase in the activity of ARMs. These experiments showed that there are at least two types of V2a neurons: excitatory V2a neurons and inhibitory V2a neurons. To corroborate this finding, a recently published paper by Hayashi et.al. revealed 11 molecular distinct sub-groups of V2a neurons. Keeping these studies in mind, we are working on identifying expression of different molecules of V2a neurons that may help in identifying different types of V2a neurons.