Arizona State University-Tempe Campus

NIH Research Projects · FY 2026 · 2024-04

understanding of biosafety and biosecurity personnel and scientific researchers (a specialized part of the U.S. scientific workforce) with oversight responsibilities or research involving Potential Pandemic Pathogens (PPPs) and Dual Use Research of Concern (DURC) in various sectors (academic, industry, government, amateur science) in the United States. The research will focus on assessing this knowledge based upon the recommendations for biosafety and biosecurity enhancements to the U.S. scientific research enterprise who fall under the U.S. Government Potential Pandemic Pathogen Care and Oversight (P3CO) policy and the U.S. Government Policy for Institutional Oversight of Life Sciences Dual use Research of Concern. We aim to: (1) examine and create a baseline understanding of the knowledge of biosafety and biosecurity practices for PPP and DURC research by biosafety/biosecurity personnel and scientific researchers, and particularly from those who specialize in DURC/PPP research across the United States; (2) explore what gaps/shortcomings exist in DURC/P3CO policies and in working-level knowledge and practices; and (3) identify recommendations for improvements to strengthen the implementation of P3CO and DURC policies for biosafety/biosecurity practitioners and scientific researchers conducting this research. By doing so, this project will create new baseline assessments of the state of DURC/PPP biosafety/biosecurity knowledge among biosafety practitioners and researchers, as well as proactively identify strengths, weaknesses, and areas for improvement in the current and future implementation of DURC and P3CO policies. For this study, we plan to use a combination of data collection methods: (1) a large-n survey sent to a minimum of 15,000 individuals working in the biosafety profession (comprising representatives from academia, industry, government, and amateur science) in the United States; (2) a follow-on survey sent to 110 biosafety professionals and scientific researchers who work on PPP and DURC pathogens; (3) a set of deliberative workshops with targeted biosafety practitioners and regulators; (4) individual interviews with approximately 30 biosafety professionals and scientific researchers working on PPP and DURC pathogens; and (5) desk research and interviews for indepth cases of institutions that conducted PPP research or have large Biological Safety Level 3 (BSL-3) and/or 4 (BSL-4) facilities conducting DURC or PPP

NIH Research Projects · FY 2026 · 2024-04

PROJECT SUMMARY An animal’s survival depends on their accurate perception of biologically relevant sounds in a complex hearing environment that is awash with background noise. Top-down contextual information modulates the processing of bottom-up sensory signals at multiple levels of the auditory pathway to improve hearing accuracy, but the neural mechanisms underlying this essential function remain unclear, especially at the earliest processing level in central auditory system. Here we examine the role of the massive top-down projection from the inferior colliculus (IC) to the dorsal cochlear nucleus (DCN). Our overarching hypothesis is that top-down signals that encode contextual information are conveyed to DCN by descending IC projections to guide context-dependent filtering. The proposed project will reveal the circuit mechanisms of top-down modulation at the first level of auditory processing in the central nervous system. This work could provide insight into ways to improve auditory perception when bottom-up signals are degraded due to hearing loss, as well auditory processing disorders for which top-down modulation has been implicated. Early filtering that is guided by top-down modulation is a general principle in sensory systems. Establishing how neural circuits implement context-dependent filtering has been difficult, due in part to a lack of in vivo phenomenological studies being combined with circuit-level analysis. We will examine the neural mechanisms underlying top-down modulation of the DCN by utilizing a powerful combination of (1) neural circuit analysis using projection-specific retrograde tracing, (2) in vitro acute brain slice electrophysiology in transgenic mice, and (3) cutting-edge in vivo electrophysiology and optogenetics in awake mice. Aim 1 tests the hypothesis that DCN-projecting IC neurons are a subtype of small cells in the central nucleus, by retrogradely labeling them and targeting them for direct electrophysiological and anatomical analyses. Aim 2 tests how descending IC projections are processed by specific cells and microcircuits in DCN. We recently discovered a pathway, through excitatory interneurons called unipolar brush cells, that could preserve the descending tonotopic signals and extend their neural representation. This aim will test the hypothesis that unipolar brush cells amplify signals to principal cells in DCN. Aim 3 tests how descending IC projections shape auditory responses of DCN principal neurons in awake mice. We will use optogenetic inhibition of DCN-projecting IC neurons to test their role in spontaneous firing rates, frequency response properties, temporal response properties, and tone-in-noise detection thresholds. Successful completion of this project will establish how top-down projections from IC modulate auditory responses in the first level of auditory processing in the brain and will reveal the circuit mechanisms that contribute to accurate auditory processing in complex sound environments.

NIH Research Projects · FY 2026 · 2024-03

10 Project Summary/Abstract Kidney diseases are a leading cause of death in the United States. Ensuring successful transplants for patients with end stage renal failure (ESRD) is a major priority of our nation, but a persistent shortage of deceased-donor kidney donations and high organ discard rates limits access to deceased-donor kidney transplants. To identify underlying causes of the non-utilization of transplant-quality kidneys, this study aims to investigate clinicians’ idiosyncratic decision-making when considering deceased-donor kidney transplant offers. Our team’s preliminary research shows that individual doctors have different ways of deciding whether a kidney is suitable for transplantation. To investigate this further, we have created a unique data set that combines information about kidney offers with on-call records of doctors who evaluated these offers, to identify the individual decision-makers evaluating each offer. This novel data linkage opens the door to an opportunity to measure offer acceptance rates and examine acceptance patterns at the individual clinician level. By analyzing these data, we discovered that even within the same medical center, where doctors have the same resources and patient populations, doctors have widely varying acceptance thresholds for donor kidney offers. The broader phase of our study has three aims. First, we aim to recruit new transplant centers to expand the individual-level, potential transplant recipient (iPTR) dataset we have begun assembling with initially enrolled cen- ters. The iPTR dataset will include clinician-level offer decisions, including refusal reasons for declined organs, as well as medical characteristics of the donors and recipients for each offer. Second, we will elicit clinicians’ be- liefs about ESRD patients’ quality-of-life while on maintenance dialysis compared to various post-transplantation health states, as well as the likelihood of kidneys of varying quality leading to better or worse post-transplant health. Third, we will identify differences in the influence of various clinical factors (e.g., donor age) and non- clinical factors (e.g, beliefs) on offer decisions across clinicians. This study investigates individual decision-making on real-world, deceased donor kidney offers for the first time. By integrating survey data capturing clinicians’ subjective beliefs about ESRD and transplant patient outcomes, we will better understand how behavioral factors impact the transplantation system alongside clinical factors. This study has the potential to make a significant impact on the health of our population by providing novel insights to guide the future development of targeted interventions that can lead to more successful kidney transplants and improved length and quality of life for kidney disease patients.

NIH Research Projects · FY 2025 · 2024-01

Project Summary Overall Core The US healthcare system struggles to provide quality care to the most underserved populations. The safety net organizations (SNOs) serving these populations have limited capacity to support scientists conducting embedded research or rapid evidence implementation. Building a research infrastructure within SNOs to create and implement culturally sensitive evidence-based approaches to improve health equity is an urgent unmet need. The US Southwest has among the worst disparities in the nation, but a robust system of SNOs. Arizona State University College of Health Solutions (ASU CHS) and Valleywise Health (VH) will create the Southwest Safety Net Embedded Scientist Training and Research (SSNE-STaR) Center. The SSNE- STaR vision is to improve the health equity of historically disadvantaged populations, with a strong commitment to the needs of American Indians, Hispanic, homeless, and other underserved populations. The SSNE-STaR will support the scientific training and professional development of Scholars embedded across Arizona SNOs to produce evidence-based research and gain proficiency in rapid-cycle implementation. The SSNE-STaR is a collaboration among academic and diverse safety net organizations including inpatient and ambulatory care health systems, federally qualified health centers, Tribal health centers, community partners, and other stakeholders, with equity as a cross-cutting focus, and primary care, maternal care, and disabilities as research priorities. ASU is a Hispanic Serving Institution focused on inclusion, health equity, and continuous improvement. VH is the only public teaching hospital and health care system in Arizona, providing services to underserved, low-income, and ethnically diverse populations, over 60% Hispanic. Other SNO partners include: Native Health of Phoenix, Inter Tribal Council of Arizona, Value Based Care, Veterans Administration (VA) Hospital, and Phoenix Children’s Hospital. The SSNE-STaR will establish a new Learning Health System (LHS) model to support physicians and scientists embedded in SNOs that provide care to underserved populations. The SSNE-STaR will recruit and train embedded health scholars throughout Arizona. Our LHS Scholars will be selected from healthcare professionals throughout the VH system and our extensive network of SNO partners. Embedded Scholars will be recruited from a network of Native American health centers and other regional SNOs. The Center will be supported by an extended research finding dissemination and implementation network of safety net organizations. ASU brings PCOR experience to the SSNE-STaR as a partner in the PCORnet Patient-Centered Network LHSnet (2015-2019), and ASU is currently engaged in a formal collaboration with PCORnet partner, the Greater Plains Collaborative (GPC) Clinical Data Research Network (CDRN).

NIH Research Projects · FY 2026 · 2023-12

Project Summary/Abstract Reducing sedentary time (i.e., sitting/lying with low energy expenditure while awake) among adults at-risk for type 2 diabetes can reduce diabetes risk and improve cardiometabolic health. Sedentary screen time (SST) (i.e., television viewing, social media, and/or video streaming outside of work and educational pursuits) is ubiquitous in American society with >80% of adults engaging in >3.5 hours/day. SST is predominantly done in the evening (eSST), where it has been hypothesized that prolonged eSST may have the most harmful health effects due in part to disruptions to glucose metabolism and sleep. Several existing interventions have successfully reduced SST, including our mHealth-based StandUPTV intervention (R01CA239612, MPI: Keadle, Buman), but intervention durations have been short (≤ 16 weeks) and no study has evaluated whether SST reductions are maintained post-intervention which ultimately are needed to lower disease risk. Forming an automatic habit by performing a new behavior after the same contextual cue is a promising strategy for behavioral maintenance. One effective habit formation strategy, often called “action planning,” has fostered behavioral maintenance across several domains (e.g., diet, physical activity, medication adherence, sleep, and smoking cessation). In preliminary work by PI Stecher, performing mindfulness meditation after breakfast or coffee in the morning increased daily meditation performance during an 8-week post-intervention maintenance period. However, implementing action plans for maintaining eSST reduction requires remote and objective eSST monitoring technologies that to date have not been available. Recently, our team has developed these technical capabilities through our StandUPTV mHealth platform that uniquely enables us to develop and test an action planning strategy for maintaining reductions in eSST. This study will evaluate the feasibility, acceptability, and preliminary effectiveness of action plans as a novel behavioral maintenance strategy in a (ORBIT Model) Phase II trial among 48 nationally-recruited adults at-risk for diabetes. Participants will be randomized to one of two mHealth study arms: (a) StandUPTV only (an evidence-based mHealth intervention to reduce SST inclusive of self-monitoring, education, and 50% reduction in SST target); or (b) StandUPTV + action plans for maintaining reductions in eSST. Participants’ action plans will use an alert (i.e. mobile notification to disrupt prolonged eSST) to cue a more active behavior (e.g.,10-min walk) and then receive a financial reward if the active behavior is successfully performed. We will use mixed methods to assess the intervention’s feasibility, acceptability, and preliminary efficacy for maintaining reductions in eSST and improving cardiometabolic outcomes. This study will contribute important new knowledge regarding evidence-based strategies to maintain behavior change, which can be applied across health behaviors. This pilot study will help to refine an mHealth solution for reducing SST that can be scaled through the use of several commercially available screen time monitoring tools and the necessary information to scale-up the StandUPTV application into a Phase III, fully powered efficacy trial.

NIH Research Projects · FY 2026 · 2023-12

Abstract Breast cancer is the most diagnosed cancer and the second leading cause of cancer death in women in the USA. Immune checkpoint therapy (ICI) is revolutionizing the therapy of a number of malignancies, including triple negative breast cancer (TNBC). Therapeutic blockade of immune checkpoints, such as by anti-PD-1, removes the tumor-initiated suppression of the immune system and unleashes prolonged anti-tumor immunity. Despite the encouraging success, many patients develop severe and sometimes life-threating adverse effects, or many also fail to benefit from immunotherapy. Existing immune therapy response predictive markers have only modest positive predictive values and there are no clinically useful markers of toxicity. Our proposed study will focus on comprehensive evaluation of serological auto-antibodies (AAb) as potential predictors of immune related adverse event (irAE) and benefit from therapy. Serum based biomarkers exploit easy sample accessibility and directly measure immune responses. Our platform can be rapidly adopted for clinical use. Our study leverages collaboration between experts on immunoproteomics and biomarker discovery and physician scientists specializing in breast cancer. Our study will analyze more than 3000 longitudinal serum samples collected from 1,195 patients enrolled in a randomized phase III trial (SWOG S1418) that tests the efficacy of one year of single agent adjuvant pembrolizumab (anti-PD-1 antibody) therapy compared to observation in high risk TNBC. Serum samples were collected at baseline and at 13 and 55 weeks after starting therapy, all samples are annotated with detailed toxicity and outcome information that were collected during this FDA registration trial. We will employ two complementary high-throughput antibody profiling technologies including Nucleic Acid Programmable Protein Array (NAPPA) and Multiple In Solution Antibody Assay (MISPA) that we developed. NAPPA allows the detection of antibodies against tens of thousands freshly produced full length proteins in hundreds of samples while MISPA enables more precise quantification of hundreds of antibodies in thousands of samples and could be scaled for clinical use. Our approach involves (i) a discovery step using the NAPPA arrays that will interrogate serum samples for AAbs against 18,000 human proteins in the discovery cohort, (ii) a verification step of identified outcome-predictive AAb using the MISPA platform using the discovery samples to select the final set of candidate markers, (iii) and blinded validation of candidate AAb using MISPA on the held out validation set. Identification of biomarkers that can predict clinically-relevant immune-related adverse events or recurrence, and hence inefficacy of therapy, would help better estimate the risk benefit ratio of these very expensive and potentially toxic therapies. Predictors of adverse events could also influence patient monitoring and allow earlier therapeutic intervention. The lessons learned from this proposed study on TNBC will likely impact a broad spectrum of other cancers since adverse events are not cancer type dependent.

NIH Research Projects · FY 2026 · 2023-12

PROJECT SUMMARY/ABSTRACT Attention-Deficit Hyperactivity Disorder (ADHD) is one of the most common mental health conditions in childhood, and nearly half of all youth with ADHD have at least one parent who also meets criteria for the disorder. Intergenerational ADHD is a significant risk factor for reduced skill use and poor child outcomes following evidence-based behavioral parent training (BPT) programs. Given that BPT is predicated on consistent, in vivo use of BPT skills, efforts to reduce parental ADHD-related barriers to BPT skill use may have significant public health implications. Incorporating Cognitive and Behavioral Therapy (CBT) strategies shown to improve adult ADHD symptoms and executive dysfunction into BPT programs may reduce such barriers. Mobile health (mHealth) adjunct interventions are another promising approach to improve BPT skill use, as personalized CBT-based supports and temporally relevant reminders may also reduce several of the most common barriers. Yet, no existing BPT program includes these augmentations. This K23 seeks to adapt an evidence-based BPT program to reduce skill use barriers and increase skill use among parents with ADHD by incorporating (a) in-session modules on adult ADHD CBT strategies and (b) a smartphone-based app that provides timely reminders and CBT-based supports. The intervention, Executive Function Enhanced Caregiver Training Skills (EFECTS), will be developed using an iterative, stakeholder driven approach that uses Human Centered Design (HCD) strategies to incorporate end-user feedback at all stages of development to improve fit, outcomes, and implementation. A pilot RCT will compare EFECTS to an existing BPT program. Candidate: My early research examined the underlying executive function deficits associated with ADHD, and my long-term career goal is to design theory-driven interventions based on our understanding of the etiological underpinnings of the disorder. As treatment research represents a shift in my research interests, I require formal, mentored training in intervention design and evaluation. Through this mentored career development award, I will learn the skills necessary to become an independent intervention researcher, including 1) mechanism-focused intervention development, 2) mHealth applications to improve parenting intervention skill use, and 3) methods to improve intervention fit, implementation, dissemination, and effects. My mentorship team is well-suited to provide the training necessary for me to become a successful intervention researcher and consists of Dr. Sharlene Wolchik (primary mentor; expertise: theory-based parenting intervention development; RCT implementation), Dr. Linda Pfiffner (co-primary mentor; expertise: developing BPT programs for ADHD), Dr. Mary Solanto (co-mentor; expertise: designing CBT interventions for adults with ADHD and executive dysfunction), Dr. David MacKinnon (advisor; expertise: analyzing treatment mechanisms and outcomes), Dr. Aaron Lyon (advisor; expertise: HCD, implementation science), and Dr. Oliver Lindhiem (advisor; expertise: parenting skill use assessment, digital health interventions).

NIH Research Projects · FY 2026 · 2023-12

Project Summary/Abstract Middle-age adults with autism spectrum disorder (ASD) have a 2.6x higher risk for early-onset Alzheimer’s disease and other dementias than non-ASD. In children, ASD prevalence is ~2%. Since ASD is a lifelong condition, there will soon be a large population of elderly with an ASD diagnosis (~700,000 in the U.S within the next decade), with a lifetime cost of ~$3.6 million per person. In a series of two papers, we recently published accelerated longitudinal decline of memory and hippocampal volume in middle-age and older adults (MA+) with ASD, compared to matched controls. This adds to our cross-sectional findings of decreased hippocampal functional connectivity and increased free-water. Free-water is a relatively novel microstructural diffusion tensor imaging technique that is more sensitive than conventional MRI metrics for detecting neurodegeneration. Our recent paper shows baseline hippocampal free-water is a stronger correlate of memory decline in our MA+ ASD than hippocampal volume. For functional MRI (fMRI), it is notoriously difficult to efficiently elicit strong hippocampal activation. Nevertheless, altered hippocampal activity and connectivity have been found in adults with ASD. We developed a relatively brief visuospatial fMRI memory task and our preliminary data show strong single subject MA+ ASD hippocampal signal. We will add this novel task and an established verbal memory task to our ongoing longitudinal MA+ ASD study. We will link fMRI data with structural MRI measures using our novel multi-modal analysis methods to identify patterns that best predict accelerated memory decline. Our long-term goal is identifying biomarkers and intervention targets for precision medicine care of aging autistic adults. The specific aims are to: 1) characterize hippocampal and memory aging trajectories in MA+ ASD, compared to matched NT adults, and 2) identify the combination of MRI measures that best predict accelerated memory decline in MA+ ASD, compared to matched NT. Our study design will use our ongoing, well-characterized longitudinal cohort of MA+ ASD (n=62) and NT (n=61) who undergo cognitive batteries and multi-modal MRI scans every two-years (ages 40-75, mean=55.2 years; M:F ratio 1.6:1; 1-4 timepoints). Our analysis approach will be a combination of hypothesis-driven, longitudinal multi-level models with hippocampal and memory scores of interest longitudinal, and exploratory whole-brain longitudinal and multi-modal partial least squares analyses. Our expected outcome is to lay the groundwork for: 1) multi-modal MRI biomarker development that can be used to predict cognitive aging outcomes, and 2) identifying targets for neuromodulation or other interventions. This is relevant to public health because findings will advance fundamental knowledge of brain aging vulnerabilities and mechanisms in MA+ ASD. This will lay the groundwork for precision medicine advances at the intersection of neuro-developmental conditions and cognitive aging, and serve as a model for other developmental conditions (e.g. Attention-deficit/ hyperactivity disorder, Cerebral Palsy).

NIH Research Projects · FY 2025 · 2023-09

It is now well accepted that structured governed dynamics modulate function, yet we still don’t know how a few changes (e.g., mutations) in sequence modify dynamics to alter function. Understanding this interplay is a key step to engineering proteins with desired function to address disease, and viral evolution to fight with endemics or future pandemics, as well as many other bioengineering applications. Despite the work of many researchers, the connection between sequence, structure and dynamics remains elusive. This is partly because there is no powerful methods that can accurately quantify each amino-acid position’s contribution to structure and dynamics. We propose to fill this gap by using an innovative, interdisciplinary method based on mathematical topology and physics based protein dynamics modeling. The guiding hypothesis is that the topological landscape of proteins governs conformational dynamics and that it can be modified with sitespecific mutations. To test this hypothesis, we will create the mathematical framework upon which the local and global topology of proteins and conformational dynamics can be rigorously associated and the evolution of the topological landscape can be quantified. This work is particularly timely for two reasons: (1) conformational dynamics have established a connection between structure and function and evolution at the proteotome scale and (2) methods from mathematical topology have shown evidence of being able to characterize protein structure. This research advances knowledge in mathematics and biology and breaks existing barriers in: (1) topology and geometry, (2) quantitative characterizations of protein structure, (3) connecting microscopic effects to the macroscopic properties of proteins and (4) providing a novel framework that enables not only to uncover the molecular mechanism of protein function and evolution based on fundamental mathematical and physical concepts, but also enables to design novel proteins with desired function. This is achieved by (1) creating novel measures of topological complexity and a mathematical topological framework for characterizing multiscale protein structure, by (2) coarse-grained modeling and dynamical analysis of proteins and (3) by combining the two to establish the connection between conformational dynamics and the topological landscape of proteins. This integrated novel framework will be tested on different protein systems with available deep scanning mutational experimental data. The successful completion of this work could lead to a breakthrough that would enable to predict and modulate protein function based on structural dynamics.

NIH Research Projects · FY 2026 · 2023-09

PROJECT SUMMARY/ABSTRACT Alcohol is the most widely used substance and the third-leading preventable cause of death in the United States. Initiation of alcohol use typically occurs in adolescence, and early onset alcohol use (< age 15) has been associated with prolonged negative outcomes such as increased risk for AUD. The development of alcohol use and AUD is influenced by genetic and environmental factors, and the complex interactions among them (i.e., gene-environment interaction or GE). Yet, the majority of genetic and GE research has been 1) conducted with populations of European ancestry, and 2) focused on alcohol outcomes among individuals who have already initiated alcohol use or developed AUD. Thus, there is limited understanding of GE processes in racially-ethnically diverse populations, and how genetic risk manifests earlier in development in order to inform early prevention and intervention efforts. Furthermore, despite culture being an important context that shapes human behavior, cultural risk and protective factors have been largely overlooked in GE research. We seek to advance the understanding of etiology of alcohol use and AUD among racially-ethnically diverse populations by taking a developmentally and culturally informed approach to study GE processes. This project draws data from the ongoing Adolescent Brain and Cognitive Development (ABCD) Study, which includes rich genomic and phenotypic data from a longitudinal sample (N = 11,875; 52.1% White, 15.0% Black, and 20.3% Hispanic/Latinx) of racially-ethnically diverse children from late childhood (9-10 years old) through adolescence. The project examines three research aims. First, we will characterize polygenic influences on adolescent alcohol use among racially-ethnically diverse youth. Using a genome-wide polygenic score (PRS) approach, we will examine the effects of multiple adult and child-based PRS for alcohol and related traits (e.g., externalizing, internalizing symptoms) on timing of progression through the stages (e.g., experimentation, initiation, regular use, and problematic use), and trajectories of alcohol use from late childhood to adolescence. Second, we will examine the role of multiple childhood precursors (i.e., impulsivity, externalizing, and internalizing symptoms) in mediating polygenic influences on alcohol use. Finally, we will investigate the role of cultural-contextual risk and protective factors (i.e., parenting, peer deviance, stressful life events, racial discrimination experiences, familism cultural value) in moderating genetic influences on childhood precursors and adolescent alcohol use. We will explore how the associations of genetic, cultural-contextual factors, childhood precursors, and alcohol use change from late childhood to adolescence by examining age and developmental differences, and exploring differences by sex and pubertal status. Findings will advance alcohol and health disparities sciences by elucidating developmental and environmental mechanisms linking genetic risk to alcohol use among racially-ethnically diverse adolescents, providing critical insights for alcohol use prevention and intervention programs, including who is most at risk, what to target, and when to intervene.

NIH Research Projects · FY 2024 · 2023-09

In this proposal, we will determine if we can use microglial extracellular vesicles (EV) that have been shed into the cerebral spinal fluid (CSF) and serum to predict the microglial activation state in the Parkinson’s brain (PD). Currently, a major obstacle in the field is that there are no established and validated methods to detect brain inflammation in response to neurodegeneration during life. Microglia, the resident immune cell of the CNS constantly patrol the brain, looking for signs of infection or inflammation caused by a host of immune stimulants. The role of microglia is to clear potential threats to the CNS, but their chemical signatures based on their presence are continuously released (cytokines). These cytokines activate neighboring microglia initiating a cascade of events that are believed to drive disease pathogenesis. Although cytokines are generally considered to function as soluble molecules, recent efforts have shown that cytokines are encapsulated in EVs. These EVs contain a host of inflammatory-associated mRNAs that encode cytokine-associated genes among other signaling molecules that are known to reflect the physiological state of the parent cell. Unlike microglia, EVs can cross the blood-brain barrier under leaky and inflamed conditions, both of which are known physiological processes in PD. To determine if we can identify microglial EVs in the periphery we will characterize EVs isolated from disease-associated microglia (DAM) using RNAseq and Liquid chromatography-mass spectrometry (LC/MS). This novel data will be used to generate targets for discovery work in the periphery. This proposal aims to address two unmet needs 1) the possibility to detect brain inflammation in the living and 2) the ability to distinguish EVs released from microglia from those released from peripheral blood mononuclear cells. We hypothesize that microglial EVs extracted from the serum/CSF will reflect EV profiles from DAM. To test our hypothesis, we propose to 1) Analyze membrane-bound proteins and EV cargo in disease-associated microglia (DAM), 2) analyze membrane-bound proteins and EV cargo in peripheral EVs isolated from PD peripheral blood mononuclear cells, 3) identify and validate unique microglial-specific EV membrane antigens for antibody discovery work and 4) use these validated antibodies to pull down microglial-specific EVs from CSF and serum. All the tissues proposed in the application were collected from premortem PD patients who have since passed and have pathological confirmation of disease without comorbidities. We believe that each of these individual aims is high impact, and novel on their own, but collectively could provide us the tools necessary to predict brain inflammation.

NIH Research Projects · FY 2024 · 2023-09

Project Summary / Abstract The treatment of children with cleft lip and palate (CLP) involves the primary surgical repairment of the cleft, followed by secondary treatments like pharyngeal flap surgery and speech therapy for the purpose of improving speech outcomes and quality of life. India has the second highest number of children born yearly with CLP in the world, second only to China. In lower- and middle-income countries like India, most of the children with CLP live in rural areas and remain untreated after the primary surgical impairment of the cleft. A study conducted from Indian Council of Medical Research (ICMR), reported that 50-55% of children with CLP require follow up surgery to improve speech, but the vast majority of these children are not treated due to limited access to clinical services. In most rural CLP cases in India, intervention ends post-primary surgery and patients and their families accept poor speech outcomes as a consequence of the disorder. This is unfortunate as, many times, these speech problems are correctable via the secondary surgery or speech therapy; however, the resources and know-how for determining who would benefit from these interventions are clustered in urban areas and those in rural regions are unaware that additional treatment is possible. To determine if secondary surgery or speech therapy are necessary, clinicians listen for signs of hypernasality in the speech produced by children. This is a subtle cue in the speech that indicates the presence of correctible velopharyngeal dysfunction resulting in reduced speech intelligibility. This is a highly specialized skill that community healthcare workers in rural clinics have not been trained in. As a result, children in rural areas go without the benefit of secondary interventions that have the potential to improve speech outcomes and quality of life. We propose an artificial intelligence (AI)-based mobile health (mHealth) application for the identification of children with CLP who would benefit from interventions to improve communication outcomes in the rural areas of India. This is a multidisciplinary, collaborative project between Arizona State University, the Indian Institute of Technology Dharwad, and All India Institute of Speech and Hearing. We will use an existing database of speech from children with CLP in Kannada, a primary language in India, to develop a hypernasality prediction algorithm for the Indian language; we will integrate this algorithm within an mHealth app (R21: SA1). We will work with AIISH to perform an initial in-clinic evaluation of the app and the algorithms (R21: SA2), then refine the app and algorithms based on this evaluation (R33: SA3). Finally, we will disseminate the mHealth app to community health workers for evaluation in rural clinics (SA4 and SA5). Milestones for development of the app and initial validation and usabililty of the app will be achieved in order to move from the R21 to the R33.

NIH Research Projects · FY 2025 · 2023-09

Project Summary/Abstract Decades of research support the effectiveness of family-centered preventive interventions (FCPIs)in limiting cardiovascular disease (CVD) risk for children. Pediatric primary care offers the ideal delivery system in which to embed effective FCPIs for CVD risk given goal alignment, parents’ trust in and longitudinal contact with pediatricians, and reach into communities facing CVD disparities. Despite endorsement from the United States Preventive Services Task Force, few FCPIs have been widely implemented in primary care, resulting in limited access for families and public health impact. This application focuses on facilitating the widespread implementation of the evidence-based Family Check-Up 4 Health (FCU4Health), a FCPI with demonstrated positive effects on family and child health behaviors. To test the FCU4Health, we conducted an RCT with 240 primarily Mexican American and low-income families in partnership with multiple primary care systems. Children were ≥5.5 to <13 years old and had elevated BMI (≥85th percentile for age and gender) at their most recent primary care visit. Compared to usual care, FCU4Health significantly improved child and family health behaviors, child social-emotional health, and resulted in meaningful change in child BMI at a magnitude consistent with other interventions. Given this positive evidence, integrated primary care organizations have become increasingly interested in its adoption. Our universal prevention trial with 217 primarily Mexican American and low-income families with a 2- to 5-year-old child is currently underway in an integrated primary care system. During these two trials, with guidance from the RE-AIM framework, and a Community Advisory Board that has overseen FCU4Health development and testing, several implementation barriers were identified affecting its reach, fidelity, and engagement. Technology-based implementation strategies (a clinical decision support tool, automated fidelity monitoring, and an Electronic Health Record (EHR)-integrated SMS text messaging platform) were developed to promote delivery at scale. In the proposed biphasic study, the R61 phase involves integration of these strategies with the EHR and usability testing. In the R33 phase, a hybrid type 3 cluster randomized factorial trial will be conducted with 150 coordinators and 1200 families to examine effects of the strategies on fidelity and engagement (primary implementation outcomes) and child health behaviors (primary clinical outcomes), family health routines, parenting skills, and child BMI. Prospective implementation cost analyses will be performed to examine the economic impact and cost-effectiveness of each strategy. Finally, we will model trajectories of child and family health behaviors and examine the associations with BMI at the 18-month follow- up. The large sample size will allow us to examine these associations by baseline characteristics (BMI, developmental stage, race/ethnicity, and gender). Results have the potential for a significant public health impact on CVD risk by facilitating the scale-up of effective interventions for families in primary care settings.

NIH Research Projects · FY 2025 · 2023-09

Abstract Profiling antibody response to disease-associated antigens is important to cancer research. In contrast to the historical approach of testing responses to individual proteins, screening and diagnosis increasingly rely on multiplexed assays to elucidate disease and patient heterogeneity. Protein microarrays allow proteome-scale screening with low sample consumption but are constrained by binding kinetics of surface-bound proteins, non-specific binding, limited dynamic range of fluorescence detection and not readily available in clinics. Peptide-based approaches limit the assay to linear epitopes. With support from IMAT R21, we have developed a next-generation, liquid-phase protein microarray platform, “Multiplex In Solution Protein Array” (MISPA), which exploits the extraordinary dynamic range of next generation sequencing (NGS) with wide applicability in both research and clinical labs. We quantitatively profiled the immune responses of oropharyngeal (OPSCC) patient and control samples using a “barcoded” human papillomavirus (HPV) antigen library for 12 HPV subtypes NGS. The assay successfully detected the positive responses in the OPSCC samples and demonstrated greater signal-to-background ratio, reproducibility, and dynamic range. Subsequently, we have advanced MISPA to assay antibody response against SARS-CoV-2, seasonal coronaviruses, and other respiratory pathogens in more than 1000 samples simultaneously as part of the NCI SeroNet with over 90% overall percent agreement with a clinical COVID-19 diagnosis and commercial EUA serological assays. In the R33 phase, we propose to further develop the MISPA platform to a fully automated research platform that is quantitative, robust, highly reproducible, high-throughput, and inexpensive for early cancer screening. We will establish SOPs for robust protein production, stable protein library storage, and minimal reagent lot-to-lot variations. We will demonstrate the versality of MISPA by increasing the barcoded protein library size to 192 by including antigens from different subtype of HPV, other viruses, bacteria, fungi and tumor antigens. We will improve reproducibility and throughput with end-to end automation for the MISPA platform to support large-scale projects requiring assaying tens of thousands samples. We will determine the limit of blank, limit of detection, linear dynamic range, precision, and other performance measures for quantitative assays. We will profile the 192 cancer related antibodies in hundreds of patients with OPSCC and cervical cancer and more than 1,000 cancer free controls and benchmark the performance with the current gold standard ELISA platform. Our experience with developing innovative high- throughput immunoproteomics platforms using laboratory automation and the quality of our preliminary data speak for our competency in implementing our proposed development. A quantitatively reproducible assay to measure hundreds of antibodies against full length properly folded proteins in thousands of individuals simultaneously will greatly benefit cancer sero-epidemiology, risk assessment and screening.

NIH Research Projects · FY 2026 · 2023-09

PROJECT SUMMARY/ABSTRACT Humans are inherently social creatures, wherein social connection (SC) is essential for optimizing well-being (WB),1–3 including positive feelings, perceptions, and functioning in daily life and the absence of psychological problems and pathological distress.4,5 This is especially critical during adolescence, a sensitive developmental period with substantial and rapid changes in their general social connections (and in peer and family domains in particular).6 Social isolation (i.e. lacking SC) is one of the primary risk factors for adolescent mental and physical health problems,7–9 while having sufficient SC is a strong facilitative factor for their long-term WB and civic engagement in adulthood.7,10,11 SC is a multi-faceted construct that includes the facets of quality of, quantity of, and need for social interactions,2,12–14 each demonstrating unique influences on adolescent health and WB.14–21 However, little is known about how these facets converge and predict (or are predicted by) adolescent WB over time or the extent by which neurobiological markers of SC relate to WB.22–24 The overall objective of the project is to evaluate the integrative facets of SC quality, quantity, and need, and their reciprocal associations with adolescent WB, in daily fluctuations, long-term trajectories, and neurobiological processes. Proposed project scientific goals are to: (1) Evaluate short-term reciprocal process between SC and WB; (2) Examine the long-term reciprocity between SC constellations and WB trajectories; and (3) Identify the convergence of behavioral and neurobiological markers of SC and their prospective associations with WB. Exploratory analyses will (a) explore modulation of these processes by sex, gender, age, pubertal status, and race and (b) consider the above three goals in peer- and family- specific SC contexts. Study design: The first two goals will be addressed via an online platform to ensure a large sample of 12-17-year-old adolescents using a 14-day daily diary design nested within a longitudinal study design (N=914 enrolled; T1-T2-T3, every 0.5 year). A subset of participants (n=104 enrolled) will also complete an in-person battery (at T1 and T3) of SC experimental tasks targeting each of the three SC facets during electroencephalography (EEG) and eye tracking (ET) acquisition. To evaluate the short-term reciprocal process, we will use daily questionnaire assessments of SC and WB to examine the same-day and next-day associations between SC and WB (Aim 1). To provide a holistic consideration of the three SC facets, we will identify adolescent subgroups based upon their experience of SC quality, quantity, and need (at T1, T3, respectively) and WB trajectories (T1-T2-T3), and examine the prospective transition probabilities between SC constellations and WB trajectories (Aim 2). Lastly, we will assess the convergence of behavioral and neurobiological markers of SC (at T1 and T3) and their prospective associations with WB (Aim 3). Significance: A deeper understanding of the reciprocal relationship between SC and WB and the neurobiological mechanisms underlying these associations will be crucial for mechanism- informed prevention programming.

NIH Research Projects · FY 2024 · 2023-09

PROJECT SUMMARY/ABSTRACT Transcranial direct current stimulation (tDCS) is a type of non-invasive brain stimulation that applies constant, low levels of direct electrical current to the scalp to modulate brain excitability. While tDCS is used in neurorehabilitation to augment motor learning, the evidence supporting this use is somewhat inconclusive, with many reports of null findings. Current study designs do not adequately control for participants’ expectations about tDCS, which may exert a powerful placebo effect on motor learning (as well as cognition, pain, or anxiety). The long-term goal is to maximize motor learning by considering person-centered perceptions of brain stimulation in addition to the effects of electrical stimulation itself. The overall objectives in this R21 application are to demonstrate that expectations about tDCS (i) play a significant role in enhancing motor learning, independent of any real stimulation (i.e., an expectancy effect vs. a treatment effect of tDCS) and (ii) can be leveraged to maximize the benefits of tDCS in enhancing motor learning. The central hypothesis is that higher expectations about the efficacy of tDCS are associated with more skill improvement following motor practice, even in placebo conditions (sham tDCS). The rationale for this project is that considering people’s expectations about tDCS will not only improve the reproducibility and rigor of future tDCS research, but will also maximize the overall benefit of tDCS for improving and restoring health. The central hypothesis will be tested by pursuing two specific aims: 1) Determine the extent to which priming expectations of tDCS will modulate motor skill learning and 2) Determine the extent to which sham tDCS improves motor skill learning. For the primary aim, suggestive information will be used to prime participants’ expectations to be more positive or more negative prior to motor training with sham stimulation. For the secondary aim, the effects of unprimed tDCS during motor training will be compared to a control group with no exposure to the tDCS device at all (motor training only). The research proposed in this application in innovative, in the applicant’s opinion, because it is a major departure from the status quo of tDCS research by considering and measuring how participants’ expectations of tDCS affect motor learning and rehabilitation, independent from any effect of the stimulation itself. The proposed research is significant because it is expected to shift current research and clinical practice paradigms that use tDCS for treatment. Ultimately, adopting an integrative health approach that considers how people’s perceptions and expectations of tDCS (and other non-invasive brain stimulation techniques) will have a greater impact on health and health care than simply focusing on parameters of the electrical stimulation itself.

NIH Research Projects · FY 2025 · 2023-09

Project Abstract Men are less likely to report TB-related symptoms, get diagnosed, smear convert, or complete treatment, suggesting that outcomes along the TB cascade are worse for men. Despite men's greater burden of TB and poorer treatment outcomes, no interventions have been developed to address these gendered disparities. Building on our preliminary research that identified men's preferences for a TB care support intervention, we identified Coach Mpilo (CM), a peer-support HIV treatment intervention that was developed by men for men in South Africa, and tailored for men TB infection. The aims of our study are to assess the feasibility of CM for men and assess secondary outcomes for treatment completion and HIV viral suppression to inform a Hybrid Type I intervention. In Aim 1, CM will be further tailored to men initiating TB treatment (CM-TB) and for with HIV co-infection (CM-TB/HIV). Using a mixed methods approach guided by ADAPT-IIT model, we will conduct interviews, CM simulations, and a pre-test to assess men's usability of CM-TB and CM-TB/HIV in this setting. We will conduct Aims 2 and 3 concurrently. In Aim 2, CM-TB will be evaluated to assess feasibility among men and secondary outcomes for retention in care and successful TB treatment (TBT) outcomes. Using a randomized controlled trial design, men (N=120) initiating TBT will be randomized to receive CM or clinic-based standard of care adherence support. The primary outcome is feasibility, acceptability, willingness and safety for men with secondary outcomes for completing TBT within 180 days per arm. In Aim 3, the feasibility of CM-TB/HIV for men (n=120) co-infected with TB and HIV will be assessed. The primary outcome is feasibility, acceptability, willingness and safety with secondary outcomes measured for proportion of men adherent to anti-retroviral therapy at TBT completion and with a suppressed viral (SVL) load 6 months post-ART initiation and post-TBT completion per study arm. If shown to be feasible, we will propose an randomized controlled trial to assess effectiveness in improving men's TB and HIV outcomes and adapted to improve men's health in the context of non-communicable diseases in South Africa and globally.

NIH Research Projects · FY 2024 · 2023-09

Project Summary The proposed project has two aims: 1) to assess the short and long-term impact of school closures mandated by the COVID-19 pandemic on students’ weight status, and 2) to examine the mitigating potential of child-targeted food assistance programs implemented during the closures. While there is some evidence that interruptions in school during summer recess result in weight gain among children in elementary grades, we have little basis for predicting the effects of a hiatus of unprecedented length, 6 months or more posed by the coronavirus. Pursuant to the Families First Coronavirus Response Act, the USDA extended the summer feeding program to deliver meals to children during the pandemic-related school closures; the USDA also authorized emergency food assistance through Pandemic Electronic Benefit Transfer cards for out-of-school children who qualify for school meals programs. While summer meals have been provided in the past to some students through summer meals programs, research establishing the adequacy of these programs in addressing weight status is lacking. To address the study aims, we will collect and analyze nurse-measured heights and weights data on children attending 120 public schools serving low-income children in New Jersey. Interrupted times series analyses will be applied to data collected prior to school closures, during four school years (SY) between 2013-14 and 2019-20 (approximately 30,000 students per year); and an additional two school years post-closure, one coinciding with the re-opening of schools (SY2020-21) and the other two years later (SY2022-23). For the same school years, we will incorporate, as covariates, annual data on school food and physical activity (PA) environments that have been found to be associated with weight status, as well as measures of the surrounding food and PA environment known to affect obesity rates. New data on operation and reach of the two federal, child-targeted food assistance programs in school catchment areas over the course of school closures will be collected to establish the mitigating potential of these programs. The proposed study will make a unique contribution to our understanding of the long-term effects of a key dimension of federal obesity policy affecting some thirty million predominantly low-income children – provision of free and reduced priced school meals. As such, the findings will be critical to our readiness to respond to future disasters and to our efforts to assure the adequacy of the food safety net.

NIH Research Projects · FY 2025 · 2023-09

Project Summary/Abstract Interdisciplinary Systems-based Training for Precision Nutrition The future workforce and current thought leaders in precision nutrition need in-depth knowledge of artificial intelligence (AI) to harness the power of modern technologies such as multi-omics and wearables to combat diet-related chronic diseases related to the mission of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Arizona State University (ASU), through the College of Health Solutions (CHS), is poised to meet this need through its multidisciplinary, transformative new structure. CHS has organized its research enterprise and academic programs into “Translational Teams,” wherein faculty, trainees, and community stakeholders work together to more rapidly translate basic discovery into practice. We propose a predoctoral and postdoctoral training program - rooted in foundational disciplines of nutrition and big data analytics - that will focus on courses and practical experiences related to precision nutrition. Students will be drawn from long- standing and successful PhD programs in Exercise & Nutritional Sciences and Biomedical Informatics, both of which are already housed within CHS and have strong collaborative ties among faculty and programmatic requirements. Our training program will provide an interdisciplinary, comprehensive training in precision nutrition topics, reflecting the expertise of our mentor team in nutrition and metabolism (obesity and diabetes; microbiome and functional foods; energy balance; wearable technologies; and digital health interventions) and artificial intelligence and systems modeling (multimodal and multiscale data integration; systems biology; actionable and interpretable AI; AI-based personalization ; time-series and mobile device analytics; and geographic information systems). The training program will support nine new predoctoral students and two postdoctoral students, each of whom will be mentored by a multidisciplinary pair of accomplished nutrition and big data analytics scientists. All trainees will be provided a hybrid-delivered “bootcamp” experience in nutrition and data science upon entry into the program to build a strong foundation for interdisciplinary training. Trainees will then sample from relevant courses in statistical and machine learning, energetics, nutrigenomics, clinical applications, adaptive trial design, etc... The training will be further supported by regular seminar series and journal clubs; experiential rotations; annual symposia; and community, industry, and healthcare-based internships. As an institution that serves >25% Hispanic population, our training program will emphasize recruitment of this and other underrepresented student populations and engage with disadvantaged communities. We will leverage our rich training environment of ongoing federally-funded projects, along with our collaborative partners at the Phoenix VA Healthcare System, and the NIDDK Phoenix Epidemiology and Clinical Research Branch, to provide trainee access to diverse datasets specific to underrepresented populations of the Southwest. This interdisciplinary training program will produce the next-generation precision nutrition scientists capable of solving the complex biomedical challenges we face in combating diet-related chronic disease outcomes.

NIH Research Projects · FY 2025 · 2023-09

Diseases that spillover from wild animals pose an increasing threat to human health worldwide, but forecasting how zoonotic pathogens spread remains a major challenge. Zoonotic diseases are complex, spatially and temporally evolving systems whose behaviors are influenced by social, ecological, genetic, and evolutionary factors. Understanding the contributions of biotic and abiotic factors to accurate disease forecasting is an urgent priority for managing the emerging risks of rapid environmental change and for improving mechanistic models of complex ecological systems. Field studies monitoring zoonotic pathogens and their host species have typically assumed that observing high host prevalence is strong evidence that (i) the host is a ‘reservoir’ of the pathogen, maintaining it at a stable population level; and (ii) the host is a persistent source of spillover into other species. However, ecological models have shown that reservoir status can be strongly context dependent, mediated by extrinsic factors including interactions with other species and habitat fragmentation. An interdisciplinary approach combining complex systems modeling, data science, and risk analysis is therefore needed to model zoonotic spillover dynamics. This project will construct novel largescale, multi-host mechanistic models of the Hantavirus Pulmonary Syndrome (HPS) and Valley Fever (coccidioidomycosis) diseases in the Phoenix, Arizona metro area to investigate the stability versus context-sensitivity of hosts as disease reservoirs and identify key future data sources required to improve forecasting and identify effective interventions aimed at reducing disease burden. Both diseases are endemic to the southwestern United States and are believed to be spread by rodent hosts, but they are caused by different types of pathogens (viruses and fungi, respectively) and show divergent case trends. The project will use Petri Net models, which are modular, scalable, and readily visualized as mechanistic network diagrams, which makes them a valuable tool for exploring how adding or removing hosts and changing land use are expected to change disease dynamics. For training and outreach, the project will implement and evaluate initiatives to (i) communicate results in public outreach events; (ii) construct a course-based undergraduate research experience (CURE) focused on disease modeling; and (iii) build capacity for researching Valley Fever and mitigating outbreaks. For outreach, the project will create a free web app for public interaction with modular disease models and their visual outputs. In addition to presenting project results for HPS and Valley Fever, the web app will be linked to an interactive textbook introducing Petri Nets to be developed by the project. For the CURE class, students will focus on synthesizing zoonotic disease data to improve risk modeling, providing urgently needed research opportunities for ASU’s approximately 7,000 in-person and online biology majors. Lastly, the project will organize a capacity-building workshop of academic and public health researchers to introduce them to Petri Net resources and to identify data needs for improved forecasting.

NIH Research Projects · FY 2024 · 2023-08

PROJECT SUMMARY Note: This project is specific to bioethics research. Existing National Comprehensive Cancer Network (NCCN) guidelines to screen patients for genetic testing that are based on family history and personal risk factors remain highly inaccurate with a positive predictive value (PPV) of less than 10%. Emerging AI models based on imaging data already show great potential to improve the effectiveness of screening patients (with improved PPV and reduced missed detections). However, the bioethics of AI-driven models and their implementation in patient care remain unregulated and unexplored. This research will research the bioethical concerns arising from using black-box AI models for stratifying newly diagnosed breast cancer patients for genetic mutation and communicating test results. NCCN has established guidelines on who is eligible for genetic testing. However, due to the inconsistent testing guidelines, more than 90% of the one million women in the U.S. who are estimated to have a BRCA mutation remain undiagnosed. Universal testing has been proposed as a possible solution. However, it is not economically feasible as it may incur up to $400 billion to perform genetic testing for all U.S. women, not to mention the lack of genetic testing for low- income populations--a large portion of population-at-risk. There are already ethical challenges that surround genetic testing, and without timely intervention, AI might reinforce or even exacerbate the outcome of patients who are already vulnerable due to existing disparities in the current healthcare system. To this end, we will focus on the following specific aims: SA1: Analyze Ethical Guidelines and Concerns with AI in Genetic Screening to assess the ethical concerns of integrating AI in recommending genetic testing and communicating the results with the patients. SA2: Managing AI Ethics–Generating Explanations, Minimizing Bias by investigating how different methods of generating explanations and best practices to minimize bias towards certain racial/ethnic groups could mitigate the ethical concerns of AI. Understanding the ethical consequences of AI in genetic screening and steps to mitigating them will be crucial in enhancing the transparency and trust in emerging healthcare technologies and guiding future policies toward effectively integrating AI in medical decision-making.

NIH Research Projects · FY 2025 · 2023-08

Project Summary/Abstract The goal of the EDRN Southwest Clinical Validation Center for Head and Neck Cancer is to improve oropharyngeal cancer screening through the rigorous validation of salivary biomarkers. The scientific approach of our Center is based on several fundamental principles. First, that human papillomaviral (HPV) infection and persistence induces carcinogenesis in the oropharynx over decades, generating well-documented circulating and salivary viral nucleic acid and serologic biomarkers. These biomarkers have not yet been tested in rigorous, prospective studies with centralized CLIA/CAP biomarker validation. Second, the low incidence requires that effective screening paradigms for oropharyngeal cancers (OPC) use novel systems for large-scale prospective studies using self-collection sampling, digital enrollment, and distributive systems to enable enrollment in underserved communities. Third, that the clinical management of positive biomarkers be rigorously addressed. Our proposal builds on our extensive experiences with cancer biomarker development, verification, validation, innovative clinical study management, and expertise in HPV oropharyngeal cancer screening. Our previous results on HPV serologic biomarkers have been confirmed in blinded phase 2 multicenter validation studies. Our results have shown that multiplexed panels of IgG antibodies for HPV16 are required for adequate predictive value. Our Meso Scale Diagnostics, LLC. (MSD®) team has fielded over 3,000 instruments worldwide, and over 700 commercially available biomarker assay kits. Their expertise at serologic assay development led to one of their V-PLEX® serology panels being selected by Operation Warp Speed as the basis of its standard binding assays for immunogenicity assessments in all funded Phase III clinical trials of COVID vaccines. We will use the MSD MULTI-ARRAY® platform to migrate the HPV serologic markers for target clinical applications in saliva. This represents an ongoing collaboration with experts in large-scale self-collection salivary biomarker screening at Arizona State University, experts on head and neck cancer screening at Baylor University Medical Center, and AT Still University (ATSU) School of Dentistry and Oral Health. We will generate high-quality well-characterized samples to validate circulating and salivary biomarkers to enhance oropharyngeal cancer screening. Adhering to the principles of PRoBE design, we will perform Phase 2 validation of HPV serology and nucleic acid testing with cancer patient and control sera and saliva, followed by developing and testing the methodology needed to conduct a prospective Phase 4 salivary screening study. We will provide a resource for expertise and clinical repository for the rigorous validation of salivary and circulating biomarkers for cancer screening.

NIH Research Projects · FY 2025 · 2023-08

Acute kidney injury (AKI) is a commonly encountered medical problem that is associated with poor health outcomes in survivors, including increased mortality and re-admission to the hospital. Despite their high-risk status, only a small fraction (<10%) of patients receive specialized nephrologist follow-up after AKI episode. The low rate of follow-up care is due to lack of clear guidelines as well as reluctance on part of patients due to several reasons such as hospital fatigue, long travel times and unwillingness to add more doctors to the care team. To address the gap in care for AKI survivors, we propose an artificial intelligence (AI) enabled, MUlti-modal SEnsor (MUSE) platform for at-home use that can monitor patient health automatically, perform risk assessment for AKI recurrence, and alert the patient to seek specialized care. MUSE comprises of – 1) a colorimetric dipstick for capturing concentration of bio-markers (creatinine, urea, pH and lactate) in urine; 2) a near-field communication (NFC) powered stretchable, battery-less, single-lead electrocardiogram (ECG) skin patch that records ECG since cardiovascular complications is a strong predictor for AKI recurrence; 3) an AI-enabled mobile application that acquires sensor data (from urine sample and ECG) and runs an on-device deep learning fusion AI model to combine sensor data and patient medical record (past co-morbidities and demographics) for precision and personalized predictions. We will harness capabilities of smartphone for several key tasks - a) capture images of the dipstick sensor with built-in camera; b) act as NFC reader for ECG patch; c) run the computer vision and AI algorithms natively on-board without requiring network connection, and encrypt patient data locally. The AI model will be trained and validated on a large retrospective dataset collected from patients at Mayo Clinic Hospital, and the sensor system functionality will be validated with an observational study on 20 adult participants (10 healthy and 10 AKI patients) at Mayo. The proposed research has the potential to drive innovations in producing the next generation of intelligent wearables that performs fusion of multi-modal sensor data and EMR for early detection of underlying health issues with high accuracy. A successful realization of the proposal aims will pave the way for a future, large-scale clinical trial with our sensor platform.

NIH Research Projects · FY 2026 · 2023-08

PROJECT SUMMARY Integrated care is critical and becoming more common, but balancing the privacy needs of patients with substance use disorders (SUD) and increasing patient safety through data access is a challenge to realizing its full potential. Our previous award (R01MH108992) found: 1) a general dearth of research on SUD data privacy and confidentiality; 2) few publications on how SUD data sharing protections impact care; 3) inadequate interdisciplinary research on SUD data protections; 4) minimal inclusion of patients and advocates in SUD data privacy literature; 5) sparse quantitative and qualitative research on SUD data privacy (opinions and legal reviews are predominant); and 6) low accuracy for electronic health record sensitive data segmentation software sponsored by the Substance Abuse and Mental Health Services Administration (SAMHSA). The focus is to amplify patients’ and providers' voices on Substance use HeAlth REcord Sharing (SHARES). Research question: Elucidate SUD data sharing challenges and the feasibility of automated data segmentation to improve data interoperability and health care for those with SUD. Study sites: three integrated clinics, a health system, a SUD treatment clinic, a health plan and a state’s health information exchange. Specific aims: Aim 1: Explore patient and provider views on SUD data sharing and health care to inform policies on SUD data privacy and confidentiality; Aim 2: Demonstrate the feasibility and accuracy of automated SUD data segmentation by proposing novel informatics methods and tools that advance automated SUD data segmentation; Aim 3: Evaluate the clinical accuracy and impact of automated SUD data segmentation demonstrating that patient-controlled data sharing improves service delivery and SUD patient outcomes. Impact: SHARES introduces novel, standard-based, institution-independent, EHR-agnostic and scalable sensitive health data segmentation methods and technology to improve data sharing and interoperability between healthcare institutions and service delivery and patient outcomes for those with SUD.

NIH Research Projects · FY 2026 · 2023-08

PROJECT SUMMARY Gaining a better understanding of the population genomic processes that shape observed genetic variation is at the heart of evolutionary biology. Over the past decades, much previous genomics work has focused on studying the causes and consequences of point mutations, utilizing single nucleotide variation to infer rates and patterns of recombination, population demographic history (modulating genetic drift), and natural selection. However, by failing to incorporate structural variants (insertions, deletions, duplications, translocations, and inversions with a length of ≥ 50 bp), the greatest source of heritable variation was often neglected, contributing to the 'missing heritability' problem faced in many studies of complex phenotypes. Owing to their size, structural variants frequently disrupt protein-coding genes and/or modify gene expression, thus their characterization is crucially important to elucidate factors related to health and disease. Several population- specific structural variant catalogues have recently started to emerge for human populations; yet, similar datasets remain limited for most non-human primates, despite their importance to evolutionary research (as outgroups to the human lineage) and extensive usage in biomedical and behavioral research. This neglect is largely owing to historical reasons, as short-read sequencing and limited sampling previously made a comprehensive quantification of genome-wide structural variation impossible. However, cutting-edge single- molecule long-read sequencing technologies now allow us to investigate the topic with considerable resolution. Over the next five years, the Pfeifer lab will combine the development of novel long-read genomics datasets with computational methods for evolutionary inference to: (i) comprehensively characterize the full spectrum of genomic variation (including the relative frequencies of different types of structural variants) in three biomedically-relevant primate species, (ii) conduct genomic-wide comparisons with hominoids to gain a better understanding of the diversity within and divergence between species, (iii) characterize the molecular and evolutionary processes determining the accrual, and dictating the fate, of structural variants, (iv) determine associations with previously characterized clinical phenotypes, as well as (v) investigate the interplay of (structural) mutation with another population genetic process that shapes genome structure, recombination. Taken together, this research will improve the utility of these species as models in biomedical research, provide new insights into the etiology of disease, and allow for a deeper understanding of the mode and tempo of evolutionary changes across the primate clade.