Pediatric sleep disorders, such as obstructive sleep apnea (OSA), impact 5-10% of children. Children are diagnosed with sleep-disordered breathing through polysomnography (PSG), which requires hours of sleep and physiology data including EEG tracings, cardiograms, pulse-oximetry, and airflow monitoring. PSG data can be used to create individualized therapy and advance the care of children with sleep disorders. To facilitate novel diagnostic and validation studies using data collected on PSG and patient questionnaires, we created a data bank of PSG and patient data. Through creating a patient database in R, we can analyze sleep behaviors and medical diagnosis of pediatric patients and support future investigations. We aimed to create a pediatric sleep disorder research database to analyze sleep behaviors of pediatric patients, hypothesizing that chronotype classification would differ with age. We created a custom R script to analyze the raw data bank for medical diagnosis, age, sex, PSG diagnosis, chronotype, patient symptom scales and reported summary statistics including count, range, and standard deviation. Dplyr and tidyverse packages were used to create data summaries and ggplot2 for graphical presentation. An initial cohort of 111 participants were analyzed for correlation of chronotype and age range (>11, <=11). Initial analysis revealed a cohort of 111 participants with an age range of 6 months to 18 years (median: 7), medical history of 15 prematurity, 16 allergic rhinitis, and 3 congenital heart disease patients, PSG diagnosis of 44 normal and 8 severe, chronotype scoring of 8 evening to 34 morning patients, OSA-18 scores ranging from 34 to 102 (median: 61). Correlation analysis revealed that chronotype distribution is statistically different between age groups. We have created a custom analysis tool to create a summary report of a new sleep data bank repository. Future studies will use the tool to inform preliminary summaries of available demographic and data. 

Vigilance refers to one’s sustained attentiveness over time. While the conceptual model and the measurement of vigilance decrement has been identified in laboratory settings, limited studies have focused on vigilance decrement within the healthcare field, specifically on medical residents tasked with providing quality care to patients over the course of long shifts and often with little sleep. We aim to investigate the effect of sleep deprivation in medical residents using data recorded from physiological sensors. Twelve medical residents enrolled at the University of Washington Medical Center completed two tasks: the Psychomotor Vigilance Task (PVT), in which participants press a button when a red dot appears on a screen, and the Electrocardiogram (ECG) Reading Task, where participants view ECG readings and determine if they display signs of Myocardial Infarction (MI). They completed each task twice; once with more than 6 hours of sleep, and once with less than 5 hours. Over each 15-minute task, we measure the participant's eye movements and physiological signals including heart rate and skin conductivity.  Additionally, we surveyed participants on the quality of their sleep from the previous two nights and their general anxiety levels through multiple questionnaires including the Pittsburgh sleep quality index. This project is currently in its data collection and analysis phase; our next steps include understanding and analyzing the relationship between variables. The findings from this study will eventually help create an intervention to alert residents when their vigilance is too low to encourage taking a break to retain better focus. 

Patient-provider communication impacts healthcare outcomes, but assessing the quality of interactions manually takes time and effort. This project explores the automatic assessment of patient-provider interactions using Language Style Matching (LSM). LSM scores the linguistic similarity of function words between conversational partners (e.g.,  pronouns, articles) from 0 (low matching) to 1 (perfect matching), reflecting how in-sync partners are. Past research establishes LSM as a marker for the quality of interpersonal communication that predicts how likely romantic relationships are to last, but has not been explored for clinical interactions. We (CL, NE) applied LSM to investigate how well patients and providers matched each other's speaking styles for insights into the quality of clinical interactions. We used Linguistic Inquiry and Word Count (LIWC), a software program for LSM analysis of transcripts. Using LIWC, we analyzed the transcripts of 108 simulated visits between 54 primary care providers and four standardized patients. We used descriptive statistics to characterize LSM across visits. Our initial findings show that LSM scores range from 0.77 to 0.94 ( mean=0.86, SD=0.03) which is similar to prior research where most verbal conversations fall between 0.83 and 0.94. These findings show that on average providers and patients tend to match each other in their speaking style at a level similar to typical conversations. However, we identified some outliers that fall below 0.83 threshold. Opportunities for future work include thin-slice analysis of the transcripts to understand how LSM scores change throughout a visit and comparing LSM scores to self-reported survey data about visit quality. ​​We hope to further investigate this efficient marker of conversational quality as LSM has the potential to characterize the quality of clinical interactions without the time and effort required of traditional manual approaches.

Current methods of cancer immunotherapy, such as CAR T-cell therapy, can treat blood cancers. However, treating solid tumors with T-cells remains a challenge, as the necrotic cores of solid tumors are a toxic environment for human immune cells. Bacteria are inexpensive, easy to genetically modify, and have many species which can colonize tumors. Bacteria, therefore, have potential to be an effective alternative to T-cell based treatments. Our challenge is to engineer E. coli bacteria to secrete immunomodulatory payloads upon colonizing the tumor microenvironment. This could be a useful avenue for immunotherapy, especially if the bacteria could produce multiple cargos with synergistic effects. However, we have limited data on what therapeutics E. coli can secrete, and whether it can secrete multiple therapeutics simultaneously. In the fall, I tested whether known E. coli secretion tags could export immunomodulatory minibinder proteins designed by the Baker lab. These minibinders interact with cytokine receptors on tumor cells and are hypothesized to reduce rates of tumor metastasis, which could make them effective anti-cancer therapeutics. Through western blot analysis, I successfully detected secretion of one of these candidate minibinders. My next step is to test whether it can be secreted together with another designed cytokine, Neo-2/15. I anticipate that combining cargos might lower each individual therapeutic’s secretion, since expressing multiple proteins may increase the cell’s burden past its secretion capabilities. If secretion or expression is observed, I will work on optimizing secretion of each therapeutic. The results of this experiment will broaden our understanding of E. coli’s potential as a delivery mechanism for individual and combined therapeutics, open future avenues to test more human immunomodulatory therapeutics and combinations thereof, and hopefully someday facilitate more effective forms of cancer immunotherapy.

Heart disease remains the leading cause of death in the United States, with the limited regenerative capacity of cardiac tissue resulting in long-term functional deficits following injury or defects. There is a critical need to develop physiologically relevant engineered heart tissues (EHTs) for disease modeling, drug discovery, and even cardiac surgery. Extrusion-based bioprinting offers a promising approach to generate EHTs with high spatial precision using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). However, most extrusion-based bioprinting methods rely on hydrogel-rich bioinks to achieve desirable rheological properties, often leading to low cell densities that limit tissue functionality. Here, we show that the cell’s properties can be leveraged to form high cell density bioinks with suitable rheological properties, without the need for excessive hydrogel content. Using these boinks, we bioprinted cardiac tissues (400 M cells/mL) around flexible polydimethylsiloxane (PDMS) posts (2mm diameter) to assess contractile force output and electrophysiological characteristics. The printed cells began spontaneously beating after two days, maintained high viability (>80%), and formed mechanically robust tissues with strong structural integrity. These findings highlight the feasibility of high cell-density bioprinting for cardiac tissue engineering and provide a foundation for future work aimed at generating complex, functional EHTs with high cell-density and spatial precision.