Hoarseness is a common symptom of multiple laryngeal diseases such as inflammation, paralysis, neurologic disease, or laryngeal cancer. Many patients with these diseases are not diagnosed with the correct underlying cause of the hoarseness early enough. Therefore, healthcare providers need better methods to screen for and evaluate different types of hoarseness. Currently, a combination of tools are used to evaluate voice disorders in specialty clinics such as patient history, perceptual voice evaluation, and laryngoscopy. We want to better understand how providers with different medical backgrounds evaluate patients with voice complaints. We are most interested in seeing how history, perceptual voice evaluation, and laryngoscopy impact decision-making and diagnosis. In addition, our group has developed a machine learning algorithm that analyzes voice to detect the presence or absence of a laryngeal mass. We want to see if this algorithm could be clinically useful for generalist providers. To address these questions, a group of clinician evaluators including general practitioners, otolaryngologists, and speech language pathologists, will be recruited remotely. Subjects will be asked to complete an electronic questionnaire with patient case scenarios, asking them to evaluate hoarse voice samples and laryngoscopy exams, with and without case history. For perceptual voice sample evaluations, clinician performance will be compared to the algorithm’s classification of whether a hoarse voice is from someone with a laryngeal mass. From there we will see if clinician detection of laryngeal masses from voice could be improved with this algorithm. If the algorithm has better performance than clinicians, then it may be clinically useful as a screening tool in the future. Our results will help us understand how evaluations for hoarseness are done and can be improved.

In 2018, tuberculosis (TB) was the leading cause of death by a single infectious disease, causing 10 million new cases and 1.5 million deaths. Upon Mycobacterium tuberculosis (Mtb) infection most people develop non-transmissible latent TB, and some develop active TB disease. Healthy hosts have a 5-15% lifetime risk of progressing from latent to active TB, and prior studies have demonstrated that a type I interferon-stimulated gene (ISG) signature can predict progression to active disease. Type I interferon (IFN) and ISG expression is induced by DNA-sensing pathways and has anti-viral functions. However, the innate immune mechanisms and genetics controlling type I IFN responses following Mtb infection are not well understood. We hypothesize that genetically regulated higher type 1 IFN responses are associated with lower anti-microbial responses and higher Mtb replication in macrophages, as well as increased risk of TB disease. To test this hypothesis, blood was collected from 40 healthy donors. Donors were genotyped using Illumina MEGA^EX SNP Array. Monocytes were isolated, differentiated into monocyte-derived macrophages (MDMs), and stimulated with 3 ligands (supercoiled plasmid DNA, cyclic guanosine monophosphate–adenosine monophosphate, and sheared calf thymus DNA) to activate DNA-sensing pathways and induce a type I IFN response. RNA was isolated at 4 and 24 hours. Interferon-beta (IFN-β) and interleukin-6 (IL-6) gene expression were quantified using Real-Time PCR. Donors had highly variable IFN-β induction upon ligand stimulation. Donors’ genotypes will be linked to these in vitro phenotypes to identify expression quantitative trait loci (eQTLs) that regulate IFN-β expression. We will assess if these functional polymorphisms in genes of interest are associated with TB disease using patient samples from a Brazilian cohort. The results of this investigation will identify novel pathways that control TB progression that can inform vaccine development and host-directed therapeutic approaches.

The quality of contraceptive counseling has been shown to influence contraceptive use. Minorities and women of lower socio-economic status (SES) report that the contraceptive counseling they received as being lower quality than white, or higher SES women. Additionally, while contraceptive counseling is usually provided through verbal interactions with the clinician, evidence shows patients value the use of visual aids, such as videos, as part of the visit. The extent to which minorities or lower SES patients are satisfied with a contraception counseling video when compared to white or higher SES women is unknown. This project compares satisfaction ratings of a hormonal intrauterine device (IUD) counseling video of minority and lower SES women to white and higher SES women. To date, 93 English-speaking women ages 18+ from family planning clinics in Chicago, Seattle and Los Angeles have been counseled by their clinician and had chosen to receive a hormonal IUD watched a 6-minute counseling video the day they received their IUD. The video showed racially/ethnically diverse clinicians who provided factual information about the IUD, and either white or Asian IUD users who described their personal experiences with the IUD. After watching the video, participants completed a satisfaction survey containing 8 categorical and 2 open ended questions. We will compare the satisfaction scores of the Likert Scale questions and use thematic analysis for the open-ended questions to determine whether attributes of the video differed along demographic lines. Of the 93 women who watched the video, 55% were white, and 45% were African American, Asian, Hispanic, Native American, or multiracial. Approximately 55% had a less than a college degree, while almost 40% reported a yearly income of $25,000 or less. Further analysis may show that certain aspects of the video are perceived differently depending on the demographics of the participants.

Lymphatic malformations (LM) are congenital vascular malformations resulting from dysregulated growth of lymphatic vessels. LM can cause difficulty in breathing, swallowing, or eating, and in rare cases, infection or death. LM are associated with postzygotic somatic mutations in PIK3CA, which encodes for the catalytic subunit of PI3K and regulates cell growth and proliferation. Three hotspot mutations cause the majority of LM: p.E545K, p.E542K, and p.H1047R. These mutations occur at very low level in the affected tissue; usually at <10% variant allele fraction (VAF) which makes diagnosis challenging. We hypothesize that variant allele fraction (VAF) differs throughout the vascular malformation and will correlate with location in the lesion. We used droplet digital polymerase chain reaction to detect PIK3CA mutations in 8 LM subjects with 95 samples including lesion, muscle, skin, and fat. We then mapped the VAF to magnetic resonance imaging to assess genetic heterogeneity by sample location. Globally, no sample had a VAF greater than 10% indicating that assaying multiple samples does not increase the observed VAF compared to other studies where one sample was assayed. Within patients, VAF varies within the lesion, ranging from undetectable to 10%. Non-lesion tissue, including skin, and most muscle samples, had no mutation detected. All four fat samples and two muscle samples were positive for mutation with VAF <2%. We did not identify any correlations between VAF level and location within the lesion. Though we didn’t identify a correlation between VAF and location in our data, these findings will be used for further analysis including understanding how cell type composition within the samples correlates with VAF. Our goal is that this dataset will help us explore the development of LM and eventually provide information useful for the advancement of targeted therapies for future pediatric patients.

Of the estimated 38 million HIV infections globally, approximately 1-2 million are people living with HIV-2 (PLHIV-2). Infection with HIV-2, which is endemic in West Africa, is characterized by lower viral loads (VL) and slower disease progression to AIDS than HIV-1. However, many PLHIV-2 eventually progress to AIDS and death if left untreated. Antiretroviral therapy (ART) for HIV-2 is complicated by fewer effective drugs and significant challenges in drug resistance testing to identify appropriate therapy. This study aims to validate a novel method of HIV-2 drug resistance testing using nucleic acid from dried blood spots (DBS). One hundred and fifty DBS have been collected as part of two clinical studies of ART for HIV-2 in Senegal. HIV viral nucleic acid (DNA/RNA) is extracted and the regions encoding protease (PR) and reverse transcriptase (RT), which are the most common drug targets, are amplified by PCR, then sequenced. Sequence data are examined for evidence of drug resistance-associated mutations. To date, we have tested 115 samples with a median viral load of 84 (range: 0-24,000) and obtained drug resistance data from 43 (37.3%). Testing was most commonly successful from samples with higher viral loads; samples with viral loads >250 copies/ml were successful 75.7% of the time. We observed several drug resistance mutations, including PR V47A (5 patients), I50V (3 patients), and L90M (2 patients), and RT K65R (4 patients), Q151M (2 patients), and M184V (10 patients). Eight patients had no known resistance mutations. Once DBS testing is complete, we will select a subset of sequences to compare to genotypic resistance testing from corresponding plasma samples to evaluate the sensitivity of DBS-based testing vs. standard methods. DBS-based resistance testing has the potential to revolutionize ART for HIV-2 by allowing faster, easier, and cheaper assessment of drug resistance to optimize second-line therapy for HIV-2-infected patients.

yEvo (“yeast evolution lab”) provides experimental evolution teaching modules to high school classrooms to bring genuine research experiences to students. Our goal is to show students how evolution can be applied to solve biological problems by asking them to carry out their own evolution experiments. Recently, we developed an experiment where students evolve yeast in wood hydrolysate media, which comes from lumber waste and contains plant sugars that yeast can convert to biofuel. Wood hydrolysate is a stressful condition for yeasts, as it contains acetic acid and harsh phenolic compounds, therefore yeast struggle to grow in this media. Mutations that yeast acquire while evolving in this media could be used to engineer a better biofuel-producing yeast. Students at Foster High (Tukwila, WA) evolved yeast in this media for seven weeks with transfers to fresh media every week. We retrieved the student’s evolved yeast and they showed striking new characteristics. We then used whole genome sequencing to identify underlying mutations. Some yeasts developed ring-like growths above the wood hydrolysate media and whole genome sequencing of two of these clones revealed mutations in regulators of cell-cell adhesion. Other yeasts reached very high population densities but whole genome sequencing revealed no novel mutations. I then utilized DNA staining and flow cytometry to check the DNA content of these evolved yeasts to see if changes in ploidy caused their new characteristics. The results showed that these yeast effectively became diploid, which has been shown by other labs to increase fitness in sugar-rich environments. yEvo is currently designing future experiments to further expand upon these interesting new results so that we can further understand mechanisms of adaptation to wood hydrolysate.

Digital health interventions are increasingly being used for diagnosis, prevention, and treatment in patients. These interventions often incorporate elements such as wearable technologies and mobile applications for more effective treatment delivery. Although the use of digital health interventions has steadily increased, challenges remain in interpreting their effectiveness due to the complexity of the data; previous attempts to evaluate this using visualization have not addressed individual patients well. Our goal was to develop a system to more easily interpret the benefits and potential improvements of an intervention on an individual basis. We used data from an 8-week digital health intervention which utilized a suite of apps called Intellicare to identify subgroups of patients based on their “trajectories”: our system utilizes clustering methods to classify patients into clusters at discrete timepoints in the intervention, based on similarities in health outcomes, intervention usage, and qualitative data, such as journal entries or communications with clinicians. Patients are split into subgroups based on their traversal through different clusters through the course of an intervention. These trajectories are then visualized with a flow-based Sankey diagram, where subgroups of users and their corresponding data, such as their outcomes, can be analyzed. Our work can be potentially expanded to other digital health interventions datasets and stands as a novel method to understand and improve patients’ experience in digital health interventions.