Duke University

NIH Research Projects · FY 2025 · 2024-09

Tobacco smoking, opioid use disorder, and chronic pain are highly comorbid conditions, and smoking cessation in this population is extremely difficult to achieve. Co-administration of nicotine and opioids results in mutual enhancement of positive reinforcement, which is likely to contribute to persistent smoking in the context of opioid agonist therapy, such as office-based buprenorphine treatment (OBBT). Moreover, nicotine has been shown to provide mild, short- term anti-nociceptive effects, which may contribute to powerful negative reinforcement in individuals with chronic pain. The goal of the proposed research is to examine whether switching to very low nicotine content (VLNC) cigarettes can directly weaken these positive and negative reinforcement cycles to improve cessation outcomes among people who smoke (PWS) receiving OBBT with non-cancer chronic pain. The research will employ a randomized between- subjects design to evaluate the effects of smoking VLNCs versus normal nicotine content (NNC) cigarettes on smoking behavior, pain, craving and withdrawal symptoms, and motivation to quit smoking. Ecological momentary assessment (EMA) will be used to examine changes in bidirectional associations between pain, timing of buprenorphine dose, and smoking urge and behavior as a function of cigarette condition. Participants will complete 1-week of baseline EMA while smoking their usual brand of cigarettes; they will then be randomized to 4-weeks of NNCs or VLNCs. EMA will continue during weeks 1 and 4 of study cigarette use. Participants will attend weekly in-person visits to obtain biomarker verification of cigarette compliance and complete self-report measures. At baseline and at the end of 4-weeks of study cigarette use, a 24-hr smoking abstinence test will be used to assess withdrawal symptoms, pain intensity and sensitivity, and demand for usual brand cigarettes. At the conclusion of study cigarette use, participants will engage in qualitative interviews about their experiences to guide treatment development, and they will be provided with nicotine lozenges to support cessation. In general, we hypothesize that switching to VLNCs will attenuate the bidirectional associations between smoking, pain, and timing of buprenorphine administration as assessed via EMA, and will lead to decreased symptoms of withdrawal and pain during the 24-hour abstinence test. We also hypothesize that VLNCs will be associated with increased willingness to make a quit attempt, and greater duration of achieved abstinence. These results will provide critical insights into the role of nicotine in maintaining smoking/pain/opioid associations and the potential for VLNCs to extinguish learned associations to promote smoking cessation in this vulnerable population.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY/ABSTRACT: Older adults account for over half of new cases of melanoma each year, but healthcare providers face significant challenges when deciding on appropriate treatment for these patients. Evidence is limited on the benefits and harms of treatment for older adults with melanoma because they were underrepresented in recent landmark clinical trials in melanoma. Effective shared decision-making is also needed to elicit the unique preferences of older adults and provide personalized treatment recommendations, but healthcare providers routinely fail to understand patient preferences when practicing routine shared decision-making. The combination of uncertain evidence and ineffective shared decision-making leaves older adults with melanoma vulnerable to over and undertreatment. In this setting, older adults with melanoma would benefit from improving the quality of evidence and implementing effective shared decision-making. “Improving decision-making for older adults with melanoma” is a two-year R03 that responds specifically to AG-24-047 for “transdisciplinary aging research that will yield pilot data and experience for subsequent aging research projects.” This proposal serves the long-term goals of aligning treatment decisions with patient preferences, maximizing use of beneficial treatments, and minimizing unwanted or ineffective treatments for older adults with melanoma. The objective for this proposal is to inform and adapt the “Better Conversations” framework for shared decision-making for older adults with melanoma. This project has two aims: Aim 1, to characterize healthcare trajectories of older adults with melanoma to inform the discussion of goals and downsides of treatment in the “Better Conversations” framework; Aim 2, to adapt the “Better Conversations” framework for treatment discussions with older adults with melanoma. The results of this proposed work will guide treatment discussions for older adults with melanoma. Upon completion of this study, we will be well-positioned to perform a pilot study evaluating an intervention to teach melanoma oncology providers to use the adapted “Better Conversations” framework for older adults with melanoma.

NSF Awards · FY 2024 · 2024-09

There is broad concern that social media inhibits productive discussion. Yet, few scholars have explored how social media platforms might be redesigned to counter such trends. This project employs generative artificial intelligence, social simulation models, and online experiments to identify how algorithms that shape the information users see on social media could promote mutual understanding, increase trust, and affect polarization. The project will also develop and, in a laboratory setting, deploy and investigate the consequences of alternative algorithms. Through these comparisons, we hope to reveal how digital platforms can promote mutual understanding and trust, informing future business leaders and policy makers within the industry. This project employs generative artificial intelligence, agent-based models, and online laboratory experiments to identify how algorithms that shape information users see online influence social norms, trust, and polarization. The first phase of the project trains Large Language Models to simulate social media users by calibrating them with empirical data derived from nationally representative surveys. Preliminary results indicate this research design can reproduce large-scale behaviors on social media platforms, and enable scholars to prototype alternative newsfeed algorithms that lead to different outcomes. The second phase of analyzes the impact of such alternative newsfeed algorithms via randomized controlled trials with human respondents via a tool that enables scholars to build social media platforms for the purposes of scientific research, and allows users to interact with such environments via mobile apps or the web. Embedded surveys will allow the researchers to assess multiple indicators, as well as behavioral data generated by research participants. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Project Summary The aim of this proposal is to plan for and deliver a proof-of-concept solution for an innovative and easy-to-use experimental platform for measuring and quantifying social behaviors in animal models. Efforts during this initial grant period will be restricted to rats and mice, experimental animals with rich social behaviors, but we hope in future iterations of this program to expand also to other model organisms, including birds and monkeys. To capture kinematic details of whole- body movement during social behaviors requires novel solutions for dealing with the inevitable occlusions that results from social interactions. To overcome the limitations of current approaches we will build and validate a novel deep neural network that learns to combine images across multiple synchronized cameras and infer the 3D physical coordinates of multiple animals. Preliminary studies have been very positive and suggest large improvements over current methods both when it comes to the range of social behaviors that can be tracked and the precision with which they can be measured. Importantly, all new technology will be readily shared with the scientific community, thereby leveraging from this single grant the potential for numerous investigators to dramatically improve the efficiency of their research programs requiring rigorous quantitative descriptions of animal behavior.

NSF Awards · FY 2024 · 2024-09

Plastic pollution is present in every corner of the planet and is routinely ingested by countless species. While there has been increasing public attention toward plastic pollution, little attention has focused on the unseen “dark matter” of the plastics problem: the thousands of chemical additives incorporated into plastic. Despite the prevalence of these additives, their synergistic and cumulative impacts across biological scales are poorly understood. The durability, persistence, and complexity of plastic additives in our ecosystems make plastic additive pollution an open-ended and intractable “wicked” problem for global security. Aligned with societally important goals of protecting the environment and promoting environmental sustainability, this Growing Convergence Research (GCR) project will illuminate the dark matter of plastic additive waste and alleviate the impacts of this waste through a Strategic Initiative to Mitigate Plastic Additive Pollution. This project will bring together a convergent team of experts in molecular and cell biology, environmental toxicology, community ecology, high-throughput chemical screens, environmental chemistry, materials science, plastic policy, environmental law, science education, and community engagement. This project comprises two phases. Phase I will focus on determining the impacts of plastic additives across biological scales through the following activities: 1) characterization of the impacts of plastic additives on cells, 2) organisms, and 3) ecological communities, 4) an assessment of the regulatory landscape of plastic additives, and 5) community level ground-truthing to assess product use and potential additive exposures in communities. Phase II will integrate knowledge gained in Phase I to develop and pilot mitigation strategies to: 6) prioritize additive combinations in need of the most urgent mitigation, 7) model novel regulatory interventions; and 8) test policies and create action plans for convergence on plastic additives. The broader impacts include deep engagement with external stakeholders across sectors to implement innovations at the local scale to reduce plastic pollution. The project will also empower students underrepresented in STEM and other to take action against environmental pollution in vulnerable communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

Our long-term goal is to decrease the weight-related breast cancer and cardiovascular disease mortality among breast cancer survivors. In the R01 application, our overall objective is to determine the real-world effectiveness of the materials developed during the R34 – Take off Pounds Sensibly (TOPS) for Breast Cancer Survivors – on weight. Our central hypothesis is that TOPS for Breast Cancer Survivors will help participants achieve significant weight loss. This hypothesis is based on the following: 1) our preliminary data show that TOPS can help women achieve and maintain significant weight loss; 2) the materials from the Moving Forward and Lifestyle, Exercise and Nutrition (LEAN) studies, which we are adapting to add to the TOPS program to create TOPS for Breast Cancer Survivors, have been shown to help breast cancer survivors achieve significant weight loss; and 3) the additional materials will be approved by breast cancer survivors who have already participated in the TOPS program. To plan to test the central hypothesis, we will pursue the following specific aims in the R34: 1) Adapt materials from Moving Forward and LEAN with the input of breast cancer survivors; 2) Conduct a pilot test of the new materials and study processes where we a) recruit participants through the electronic health record; b) convert from a peer-led to a dietitian-led intervention for the initiation phase; c) transition participants to TOPS chapters in the community for the maintenance phase; and d) collect data in a virtual setting and in the community. The proposed research is innovative because for the following reasons: 1) it combines efficacious weight management programs developed at academic institutions with a low-cost community-based program with national infrastructure; 2) it transitions participants to a maintenance phase in their communities where they can receive ongoing support indefinitely; 3) it collects data via Bluetooth scales and at LabCorp and Quest Diagnostics sites in their communities; and 4) to our knowledge, this is the first study to test a sustainable weight management intervention for breast cancer survivors. The proposed research is significant because it will develop the evidence base for a scalable weight loss intervention among breast cancer survivors. The rationale for this project is that the successful completion is expected to provide evidence that a community-based, weight loss program with a national infrastructure can help breast cancer survivors manage their weight, which may decrease their breast cancer recurrence and mortality; reduce CVD morbidity and mortality; and improve their quality of life.

NIH Research Projects · FY 2024 · 2024-09

The opioid crisis is a pressing global issue, highlighting the need for exploring new targets with unique mechanisms of action. Extracellular adenosine is known to relieve pain by activating adenosine A1 receptors (A1R). However, the development of synthetic A1R agonists for pain relief has been hampered by challenges such as side effects or tolerance. Therefore, a novel strategy for A1R activation is imperative. Equilibrative nucleoside transporter 1 (ENT1), the main cellular adenosine transporter, offers a potential avenue. By inhibiting ENT1, extracellular adenosine concentrations will increase, potentially leading to analgesic effects through A1R activation in primary sensory neurons. This concept mirrors monoamine reuptake inhibitors used for neuromodulation. Based on the crystal structure of human ENT1 in complex with a clinical inhibitor dilazep, we modified dilazep. Our modified inhibitor, termed JH-ENT-01, has shown analgesic efficacy in an animal model of neuropathic pain, unlike dilazep. This indicates ENT1 as a potential neuropathic pain target, and the rational design of ENT1 inhibitors may be a promising pathway for neuropathic pain treatment. Our logical next steps are to validate ENT1’s role in neuropathic pain, to understand the mechanism of our new ENT1 inhibitor, and to develop in vitro screening methods. We have three aims: 1) In vivo validation of ENT1 as a novel target for neuropathic pain; 2) In vitro and ex vivo characterization of ENT1 inhibitors; 3) Development and improvement of in vitro assays. We have assembled a diverse, multidisciplinary team to achieve these aims. Our findings during this funding period will form the foundation for a subsequent U19 application (RFA-NS-22-052) with the following four components: 1) Validation of Therapeutic Target and Underlying Biology; 2) Development and Validation of Animal Models and/or Outcome Measures; 3) Assay Development, Screening, and Early Optimization; 4) Pharmacokinetic/Pharmacodynamic (PK/PD) and Efficacy Studies. Addressing the issue of neuropathic pain is important, and our work will deepen our understanding of the role of nucleoside transporters in pain modulation, paving the way for developing non-addictive pain medication in the future.

NIH Research Projects · FY 2025 · 2024-09

The current state of genomic diagnostics allows us to diagnose genetic disease early in life either by 1) identifying carrier status for inherited diseases before conception or 2) diagnosing a genetic condition during pregnancy. Genetic diagnosis at these two timepoints is inconsistently offered and without early diagnosis, early treatment including new in utero therapies may not achieve their full potential. The central hypothesis of this grant is that newly developed strategies can improve risk stratification and identify appropriate candidate individuals for early genetic testing in order to achieve earlier diagnosis and allow for earlier treatment. Aim 1 will reevaluate which inherited genetic conditions warrant universal preconception carrier screening. Preconception carrier screening guidelines for inherited diseases can be refined in such a way as to resolve conflicting guidelines from various medical societies, add important actionable genes that have early treatment available, and ultimately improve uptake of this available testing. In order to achieve this, two biobanks (that improve upon prior work) will be used to update carrier frequencies for the genes previously recommended for universal carrier screening. Expert mentorship and didactic work in genomic epidemiology and large biobank dataset utilization will accomplish this aim and work toward building a genomic medicine public health research program. Aim 2 will use pregnancy health factors to identify fetuses and neonates at higher risk for genetic disease. Pregnancy health factors such as preeclampsia, gestational diabetes, and placental abnormalities, may serve as indicators that a pregnancy is high risk to be affected by a fetal genetic disease. Utilizing these factors in a machine learning model will enable risk stratification of a pregnancy into low, moderate, or high-risk for fetal genetic disease. The machine learning model will be built using a prior studied cohort of 236 pregnancies affected by fetal genetic diseases and 472 unaffected pregnancies. The model will be validated using a cohort of 325 mother-infant dyads with suspicion for genetic disease; 20 dyads in the validation cohort will be consented for return of results. Expert mentorship and didactics in statistical modeling and machine learning will accomplish this aim and work toward a research program that utilizes machine learning with existing and available health data to advance the diagnosis of rare disease on a public health scale. The aims of this proposal coupled with a mentorship and training plan will establish an independent research program focused on genomic epidemiology and machine learning to launch public health measures that advance the diagnosis and treatment of genetic disease in early life.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY AND ABSTRACT Chronic low back pain (LBP) is extremely common worldwide yet poorly understood due to its multidimensional complexity of biological, psychological, and sociological factors. Social determinants of health are non-medical factors that influence health outcomes and can be measured partly by sociodemographic factors and social health factors (i.e., social isolation, social participation, and social roles). An individual’s social factors may interact with that individual’s biology during an acute LBP episode by influencing specific immune responses, which may provide two pathways for interventions to prevent the development of chronic LBP. The goal of this proposed project is to elucidate the relationship between social and biological factors’ influence on the transition from acute to chronic LBP. To achieve this goal, we will use extant data on participants experiencing an episode of acute LBP and determine if sociodemographic and social health factors influence the transition to chronic LBP at three months. Then we will begin exploring the role of the immune system in this model by cross-sectionally examining the association between social factors and the immune system during an acute episode of LBP. The overall hypothesis of this proposal is that social factors are directly associated with the transition from acute to chronic LBP and may influence biological pathways initiated during an acute episode of LBP. This proposal will achieve the goals of the proposed study in three specific aims: first, by determining if race or racial identity is associated with the transition from acute to chronic LBP; second, by determining if social health factors are associated with the transition from acute to chronic LBP, as well as examining the potential moderating effect of race on this relationship; third, by identifying the relationships between social factors and the immune system during acute LBP. These aims will advance our knowledge of risk factors for developing chronic LBP. The proposed work will have broad clinical implications for treating, managing, and/or preventing chronic LBP through potential targeted treatments of social health risk factors (i.e., isolation, roles, or participation) or potential immunotherapy treatments for sub-groups (i.e., race) at high risk for transition to chronic LBP.

NIH Research Projects · FY 2024 · 2024-09

PROJECT SUMMARY A basic understanding of human neural circuit design is essential to grasping neurological disease processes and developing targeted brain therapies. However, due to a shortfall of human experimental platforms, scientists have investigated neural circuits in model systems that lack the constituent cell types and organizational principles of the human brain. As a result, we are left with a poor understanding of how human neural circuits are configured, impacted by disease processes, and targeted for therapeutic purposes. Here our laboratory will pursue innovative research approaches that specifically advance our knowledge of human neural circuits. Using live brain specimens collected from neurosurgical procedures and CellREADR, a novel genetic tool for cellular access, we will investigate the cellular properties and circuit functional roles of human cortical interneurons. Cortical interneurons are the principal inhibitory cellular elements of neural circuits in the cerebral cortex, and their dysfunction has been implicated in various brain disorders such as epilepsy, autism, and Alzheimer’s. While scientists have detailed interneuron form and function in laboratory mice, the cellular diversity, circuit functions, and pathophysiologies of human interneuron populations remain almost entirely unknown. Here, using a range of anatomical, physiological, and transcriptomics methods in ex vivo human brain tissues, we will generate a multimodal phenotypic catalog of human interneurons and characterize their functional organization in neural circuits. These studies will advance generalizable strategies for human cellular and circuit neuroscience, while furthermore yielding a fundamental understanding of human interneurons and human inhibitory circuit design.

NIH Research Projects · FY 2024 · 2024-09

Project Summary Sickle cell disease (SCD) is severe hematological disease seen in Sub-Saharan Africa and the U.S., affecting up to 3% of the newborn population Africa. In the US, the disease affects 100,000 Americans, but is disproportionately borne by African Americans with one out of every 365 African-Americans affected. SCD patients experience vaso-occlusive crises (VOCs) as acute bouts of pain. Currently, the only cure for SCD is a bone marrow transplant but the cost and difficulty of this procedure results in SCD patients often choosing to instead manage their disease with treatments and routine monitoring. However, there is currently no objective measure or lab test to determine VOCs and associated pain severity. Routine monitoring of red blood cell (RBC) health, however, could provide a window for avoiding VOCs and enable more thorough, quantitative analysis of therapeutic interventions. The goal of this project is to create a prototype device suitable for use at the point of care for monitoring SCD patients by advancing a novel technology for rapid, automated, high throughput red blood cell (RBC) imaging. The approach is based on quantitative phase imaging (QPI) to create a hologram of every individual RBC in a sample. These imaging data can then be evaluated for the proportion of sickled cells using machine learning. Further, since up to 10^6 RBCs are imaged in each sample, a large volume of data will be obtained, suitable for developing AI algorithms that can reveal additional information. Significantly, the device is compact and low cost, indicating that there is significant potential for translation to a point of care device. In this project, we will seek to translate our technology, with demonstrated high throughput imaging capabilities, to realize a prototype device suitable for use in clinical trials. They key development steps include development of the imaging device to enable a robust, compact form factor, advancing the design of the microfluidic device that carries the RBC's and creating new and improved algorithms to enable high throughput segmentation and analysis of the obtained imaging data. The output will be a prototype device for assessing the RBC health of individuals based using a small blood draw which can be analyzed locally to enable treatment decisions more quickly.

NIH Research Projects · FY 2025 · 2024-09

Hypertension is a leading cardiovascular disease risk factor in the US and a leading cause of stroke, kidney disease, and heart failure. It is more prevalent, severe, and uncontrolled in Black individuals, who have almost two-fold higher age-adjusted mortality for hypertension-related cardiovascular deaths. Clinician unconscious assumptions contribute to disparate health outcomes, including hypertension. Because hypertension is most often treated by primary care clinicians, this group is a high priority for intervention to mitigate impacts of unconscious assumptions. Therefore, to address disparate cardiovascular health outcomes, it is crucial to mitigate the impact of unconscious assumptions among primary care clinician providing hypertension care. We have developed and pilot-tested an evidence-based curriculum designed to teach practicing clinicians mitigation skills. Preliminary data in a group of non-primary care clinicians indicated that clinicians are motivated to address unconscious assumptions in health care, that the evidence-based curriculum is feasible and acceptable, and that clinician confidence in providing care increased. In order to proceed to a definitive clinical trial to test the hypothesis that the curriculum will increase use of mitigation skills by primary care clinicians and address disparate hypertension outcomes, further work is needed and will be accomplished in the proposed project. We will refine and standardize the curriculum, and additionally develop and standardize protocols for a) clinician enrollment and engagement; b) patient enrollment and engagement; c) implementation of patient-initiated audio-recording of clinical encounters; d) analysis of recorded encounters; e) extraction of blood pressure and other data from the EHR; and f) data management and analysis. We will pilot these protocols in two primary care clinics, enrolling 20 primary care clinicians and 100 patients. Outcomes will include an objective measure of mitigation skills immediately after completing intervention and EHR-generated assessment of hypertension control 3 and 6 months after intervention. In addition, we will obtain qualitative feedback from clinicians on study procedures, curriculum content/format, and ways to sustain learning. The proposed work is necessary and sufficient for the conduct of a subsequent full-scale randomized trial to test the hypothesis that the evidence-based curriculum will increase use of mitigation skills by clinicians and ensure that all patients have the same opportunities to fully pursue their goals for hypertension treatment and control.

NIH Research Projects · FY 2024 · 2024-09

ABSTRACT Malignant phyllodes tumors (MPT) are extremely rare primary breast cancers, which are globally understudied due the infrequency of the diagnosis and to the unfamiliarity with the aggressiveness of their biologic behavior. Because benign phyllodes tumors are much more common and have a very indolent course, MPT are often underappreciated. There are few reliable predictive markers for outcomes and unfortunately local recurrence (LR) and/or distant metastases occurs frequently for MPT (20%). Treatment is almost exclusively surgical; there is no systemic therapy outside of the metastatic setting. With no known effective chemotherapy and no approved targeted therapy options, metastatic progression, which occurs frequently, portends a dismal prognosis. Median survival for these young women (median age of 45) is just 7-15 months. This study will define clinically actionable opportunities for this rare, but frequently fatal, tumor, in a population currently not provided access to potential life-saving therapies due to severely limited data. This study specifically aims to define potentially targetable and actionable opportunities and to improve the ability to predict outcomes (both phenotypically and genotypically). The results may be immediately impactful by allowing known FDA-approved therapies to be offered to those known to be at highest risk. Our first aim is to define the repertoire of genomic alterations in borderline PT (BLPT) and MPT and to evaluate these for associations with clinical outcomes. This study will be conducted as an archival project (utilizing stored tissue blocks) and will include 100 MPT and 50 BLPT cases, with at least 25 cases having known LR and/or metastatic disease. Our second aim is to assess the reclassification and outcomes of phyllodes tumors applying the newly released College of American Pathologists (CAP) Phyllodes Cancer Protocol Template and to develop a predictive model and nomogram. Utilizing all available cases of BLPT and MPTs previously included in our group's US multi-center phyllodes tumor series (N=550), we will perform a centralized histopathologic re-review, based on digitized whole slide imaging and classification according to the new CAP reporting protocol. Without an accurate and reliable predictive model for LR and/or metastatic disease, we remain unable to stratify women for interventions. Our group's ability to perform molecular sequencing on a large number of BLPT and MPT will not only help define who is at risk, but who may immediately be eligible for systemic therapies, not currently available to women at high risk for an unforgiving clinical course.

NIH Research Projects · FY 2025 · 2024-09

Access to kidney transplantation as a cure for end-stage kidney disease is severely limited by donor organ shortage. Exacerbating this shortage is the need for re-transplantation resulting from (1) early graft failure due to poorly controlled alloimmunity; (2) late graft failure due to progressive organ fibrosis and occlusive vasculopathy, two characteristics of chronic allograft nephropathy (CAN). Our preliminary studies from murine models suggest that following organ anastomosis in kidney transplantation, the temporal process of initial recipient monocyte infiltration followed by their ensuing differentiation to pro-inflammatory macrophages and later macrophage maladaptive response to injuries plays a significant role in promoting alloimmunity and CAN. Specifically, following the initial recipient monocyte infiltration, a monocyte cell-surface receptor tyrosine kinase called AXL determines the ability of the infiltrating monocytes to differentiate to pro-inflammatory macrophages and promotes alloimmunity. Consequently, inhibiting AXL early post-transplantation significantly prolongs rejection-free allograft survival. Later, in response to injuries such as ischemia, a macrophage intracellular protein called Allograft Inflammatory Factor 1 (AIF-1) determines their maladaptive response to ischemia and promotes kidney fibrosis. Consequently, inhibiting AIF-1 during kidney ischemia significantly reduces long-term kidney fibrosis. These observations led us to hypothesize that in kidney transplantation, AXL determines early macrophage differentiation and function, whereas AIF-1 determines late macrophage response to injury. Thus, a potential therapeutic opportunity is to sequentially block AXL and AIF-1 in kidney transplant recipients to (1) inhibit early alloimmunity and (2) prevent late kidney fibrosis. In this new R01 application, we propose two specific aims that will: (1) determine mechanisms of AXL in early post-kidney transplant period on promoting alloimmunity. This aim will also determine extracellular and intracellular AXL signaling partners, and test clinical values of murine findings in human monocyte cell lines; (2) determine mechanisms of AIF-1 in late post-kidney transplant period on promoting maladaptive response to injuries and allograft fibrosis. This aim will also determine AIF-1 signaling components that control kidney resident macrophage functional outcome, and test clinical values of murine findings in human cell lines. Our experienced team of investigators include the PI Dr. Luo, an expert in transplant immunobiology who will direct all immunological studies in murine kidney transplant models, and the co-I Dr. Privratsky, an expert in acute and chronic kidney injury models who will direct all studies of post-kidney transplant injuries and measurement of long-term kidney allograft fibrosis and function. Our ultimate goal is to identify new temporal and pathway-specific targets for modulating macrophage functions post-kidney transplantation. These studies will pave the road for future designs of macrophage-targeting therapeutics for enhancing kidney allograft survival, thus reducing re-transplantation need.

NIH Research Projects · FY 2025 · 2024-09

Project Summary Behavior dysregulation emerges early in life, is persistent, and often has lasting associations with more intractable symptoms of child psychopathology. Attempting to isolate early risk factors is difficult due to the varying presentations of behavior dysregulation, including comorbidities, differing developmental trajectories, and varying responses to treatments. Consequently, it is vital to isolate early developmental risk groups by characterizing early risk mechanisms in clearly identifiable subgroups at risk for behavioral disorders, such as children with prenatal nicotine exposure. These children are at least 1.5 times more likely to develop attention- deficit hyperactivity disorder ADHD, and often display early attention, motor, and sleep dysregulation commonly associated with the disorder. Yet, the etiological pathways from infant risk factors to later behavior dysregulation are unclear. Attention, motor, and sleep behavior are transdiagnostic constructs that are hypothesized to underlie many behavioral disorders, including ADHD, and internalizing and externalizing disorders. Thus, they may be important phenotypic markers predicting early behavior dysregulation. This K99/R00 proposal will help to identify infants who may develop persistent behavior dysregulation from infants exhibiting transient behaviors or no/low risk symptoms through multiple Research Domain Criteria (RDoC) constructs. This project seeks to incorporate more sensitive, scalable, and automated measure to identify behavior dysregulation earlier in development than current assessments allow. The first aim (K99) will leverage integrative data analysis techniques to identify modifiable, RDoC-informed mechanisms that increase risk for behavior dysregulation in toddlers. Through data aggregation and advanced statistical techniques, this project will uncover meaningful insights from a large integrated cohort (N > 3,000) composed of multiple independent datasets on nicotine exposure during pregnancy and neurodevelopmental outcomes from birth to 3 years of age. The second aim (R00) will track trajectories and timing of attention, motor, and sleep behavior using early, sensitive, and scalable measures to pinpoint risk for later behavior dysregulation in infants exposed to nicotine in utero. For this aim, we will recruit a new sample of birthing parents and infants across the first postnatal year (< 5 days old, and 4-, 8-, and 12-month-old infants) to characterize trajectories of attention, motor, and sleep to determine how these trajectories predict risk for behavior dysregulation at 1-year of age. To accomplish these aims, the mentored training will focus on furthering knowledge on the risk phenotypes of behavior dysregulation, and data harmonization and advanced causal modeling. This project will provide advanced training to support future research on using sensitive, scalable, and automated measures to detect risk for psychopathology in early childhood. It will also aid in prevention and intervention efforts by isolating early mechanisms of risk, as well as malleable behavioral targets, a NIMH priority (Strategic Objective 2.2).

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT The wildland-urban interface (WUI) fire of August 8, 2023 in Maui has been the deadliest in US history. It completely swallowed the historical town of Lahaina, claimed > 100 human lives, burned > 2000 structures, and displaced thousands of people. The combustion of biomass, buildings, furniture, and automobiles generated a complex mixture of toxic compounds including lead, arsenic, asbestos, cyanates, dioxins, and flame retardants. These compounds are enriched in the ashes, residues, and soils in the burn site and surrounding areas. As of January 2024, most of the burn site remained inaccessible to the public as residues/soils were deemed toxic. Many of these toxic compounds are persistent in the environment, causing cumulative exposures years and even decades after the fire. The over goal of this time-sensitive R21 project is to address fire victims’ current and future health concerns. We formed an academia-government-community partnership to pursue the following aims. Aim 1: To assess the impact of the fire on mental and respiratory health outcomes. We plan to enroll child and adult participants from 100 fire-displaced households and 100 nonaffected households. Respiratory symptoms, lung function, and mental health and wellness will be assessed using the NIEHS Disaster Research Protocols (the RAPIDD toolkits) with modifications to clarify the specific disaster setting. We hypothesize that individuals who experienced displacement would have worse mental and respiratory health outcomes than individuals without displacement experience, adjusted by demographics, lifestyle, socioeconomics, pre-fire environmental exposure history, and pre-fire health status. Aim 2: To assess the change in health outcomes one year later, relative to baseline impact assessed in Aim 1. We hypothesize that individuals affected more severely at baseline would have worse respiratory and mental health outcomes at follow-up compared to those affected less severely. Aim 3: To collect house dust, wristband, and biological samples for future investigations of longer-term health effects of cumulative and/or changing exposures resulting from the disaster. These samples can be analyzed to capture important time windows of toxic exposure and biomarkers indicative of toxicity and disease risk. They will be integrated in our planned R01 that aims to address lasting concerns of the victims while studying the interactions between respiratory and mental health outcomes in toxic exposure and disaster contexts. Overarching Aim: To report the study findings back timely to study participants and the larger community. We will form a dissemination team with extensive experience in bi- directional communications between researchers and the community. This team will answer participants’ and community questions in a proper manner taking into considerations of culture sensitivity. The team will facilitate receiving input from the community to help develop survey instruments and collect data and samples. The data and samples will be an invaluable resource that can be shared with other researchers who wish to study WUI fires that are becoming increasingly common. 1

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Thousands of children in the United States receive treatment for acute pain each year, and even more experience pain that is unrecognized. The majority of those who are treated receive interventions that have not been proven to be safe or effective in children. Inadequately treated acute pain can lead to chronic pain and multiple adverse outcomes. Rigorous trials performed under a structured research and data ecosystem that address acute pediatric pain are urgently needed. To address this critical, unmet public health need, we will establish the Duke-Utah HEAL Kids Pain Resource and Data Center (Duke-Utah RDC). Our RDC will support multi-site clinical trials within the HEAL KIDS Acute Pain Clinical Trials (APCT) with the overarching goal of harmonizing these trials in an integrated program with shared objectives, procedures, and tools to maximize knowledge gained in pediatric pain. The Duke University Clinical Research Institute (DCRI) and The University of Utah, two powerhouses in clinical trial and data coordination with an established history of collaboration, are uniquely positioned to establish this RDC. We will leverage the capacity and experience of the world’s largest academic research organization, DCRI, and the pediatric trial and informatics expertise of the Utah DCC to integrate high-quality logistics and operations, experienced communications management, and sophisticated data and informatics solutions for the HEAL KIDS program. The faculty on our proposal, who include pediatricians with subspecialty expertise (Greenberg, Benjamin, Watt) and experts in informatics (Sward), will provide coordination, support, and consultation to HEAL KIDS program investigators and trial data coordinating centers, building their capacity to implement well-designed, efficient trials that produce high-quality, easily accessible data. To achieve this vision, we will establish 2 RDC cores: 1) a Data Curation and Harmonization Core; and 2) an Administrative and Communications Core. These cores will engage proactively with the ACPT trial teams, data coordinating centers, NIH, and other stakeholders to accomplish the following specific aims: 1) Create and sustain a harmonized HEAL KIDS Pain research and data ecosystem; 2) Facilitate compliant data sharing and seamless accessibility to maximize future research; and 3) Support effective communication within the program and to the broader research community. Our HEAL KIDS Pain research and data ecosystem will lay the foundation for the generation of harmonized data. We will ensure that these data are submitted to public use repositories and easily accessible to investigators through the use of sophisticated informatics tools. The Duke-Utah RDC will fuel the successful completion of the awarded APCT trials while maximizing the impact of the resulting data to ensure forward progress in the management of pediatric pain conditions. Our infrastructure will serve as a model and foundation for future pediatric pain trials.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Safe and effective analgesia after surgery is a paramount yet unmet medical need in children, and poorly treated pediatric pain remains a persistent and significant public health concern. For moderate to severe acute postoperative pain, opioids remain the mainstay and most efficacious systemic pharmacologic therapy. However, adequate age-specific pediatric pain treatment is limited by insufficient understanding of the dose-dependent clinical effects of even widely available opioid analgesics. Every day, anesthesiologists and surgeons who care for children are forced to navigate these knowledge gaps – to ensure adequate pain relief while balancing concerns for opioid side effects and harms, patient and parental satisfaction, adherence to practice guidelines, and compliance with legislative statutes that restrict opioid prescribing. The challenge to determine the optimal analgesic regimen after pediatric surgery is particularly critical for the 500,000 children who undergo tonsillectomy annually. Tonsillectomy is the most common painful elective procedure in children, yet analgesic management after surgery remains inadequate, insufficient, and without consensus. Specifically, opioid prescribing practices after tonsillectomy are highly variable, largely dependent on individual surgeons, and susceptible to both over- and under-prescribing in an era when opioids have become a major public health concern. We must do better. A recent paradigm shift in perioperative opioid use in adult surgical patients – the use of long-acting (methadone) over short-acting opioids – has successfully diminished acute and chronic postoperative pain and enabled less post-discharge opioid prescribing. Methadone, and its superior pharmacologic properties, fulfills the therapeutic goal to better match the duration of analgesia to duration of pain. Nonetheless, despite well-established benefits as a highly effective perioperative analgesic in adults, methadone use to treat surgical pain in children has been limited to major inpatient procedures and hampered by the lack of robust clinical data. Our proposal will address the urgent need to determine the optimal age- specific, weight-based dose of intraoperative methadone for outpatient tonsillectomy in children in order to improve analgesic outcomes and decrease the need for postoperative take-home opioids. We propose a single center, randomized, double-blind, parallel-group, dose-finding trial of single-dose intraoperative intravenous (IV) methadone compared with short-acting opioids in 396 children in three age-matched cohorts. We will 1) determine the optimal age-specific intraoperative dose of IV methadone in pediatric tonsillectomy that results in less postoperative pain and opioid use compared with short-acting opioids, and 2) assess the impact of long- versus short-acting intraoperative opioid on post-tonsillectomy recovery outcomes. After study completion, we expect to have enriched the understanding of pediatric methadone pharmacology, improved analgesic outcomes in a population of children and adolescents with acute surgical pain, enabled reduced postoperative opioid prescribing, and achieved safer and more effective pediatric precision medicine.

NSF Awards · FY 2024 · 2024-09

Since World War II, the U.S. federal government has been the country’s largest funder of research and development (R&D), with tripartite objectives of national defense, health, and economic growth. Much attention has been given to studying large expansions of federal R&D investment. Yet federally-funded R&D, as a share of domestic R&D or GDP, has been on decline since its twentieth century peak—an era which has also seen a major reorientation of the federal portfolio from defense to biomedicine. The consequences of these changes are not yet fully understood. A traditional economic view is that public R&D fills gaps that markets leave behind. However, since the 1950s the U.S. (and global) innovation system have matured significantly, potentially leaving fewer gaps for public R&D to fill. Moreover, though the impacts of past public R&D investments often inspire new ones, there is less evidence on the effects of public R&D drawdowns and budget cuts to inform choices when renewing funding for programs. This research evaluates the impacts of declining public R&D and the pivot away from defense innovation. Using recently-collected long-run data on government-funded invention, paired with new data on the postwar scientific workforce, this project examines (i) the effects of federally-funded R&D on the development of complex, science-based technologies (i.e., “deep tech” innovation); and (ii) the impacts of large and abrupt cuts to defense research in the 1970s (before which the Department of Defense was nearly as large a funder of basic research as NSF) on the U.S. innovation system, including science, scientists, universities, and high-tech industries. The project results improve understanding of science and technology with new evidence on the effects of government withdrawal from R&D funding and produces new approaches to evaluating public-private R&D spillovers and identifying regularities and tensions that inform future policy choices. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This award funds research in macroeconomics and bounded rationality. The team starts with the observation that in the formalization of representativeness (Kahneman and Tversky, 1972) developed by Gennaioli and Shleifer (2010), overreaction and confidence are affected by uncertainty, as a news effect interacts with an uncertainty effect. In the time series domain, this interaction emerges in a smooth version of Diagnostic Expectations (DE). Under Smooth Diagnostic Expectations (Smooth DE), agents overreact to new information. Since new information typically changes not just the conditional mean, but also the conditional uncertainty, changes in uncertainty surrounding current and past beliefs affect the severity of the DE distortion and confidence. As a result, Smooth DE ends up connecting two vastly popular branches of Economics that have largely proceeded in parallel: the Diagnostic Expectations literature and the Uncertainty literature (Bloom, 2014). The research consists of three projects. In the first project, the team highlights the inherent link between representativeness and uncertainty and introduces Smooth DE as the natural time series formalization of such a link. Under Smooth DE, agents over-react to new information as captured by the change in the current distribution of future events with respect to a reference distribution. Changes in uncertainty surrounding current and past beliefs affect the extent of the DE distortion. Smooth DE implies a joint and parsimonious micro-foundation for key properties of survey data: (1) overreaction of conditional mean to news, (2) stronger overreaction for weaker signals and longer forecast horizons, and (3) overconfidence in subjective uncertainty. In the second project, the team studies quantitative business cycle models that leverage insights from the Smooth DE framework, as well as from the team’s previous work on DE, imperfect information, and non-linear solution methods. The goal is to provide a rigorous and parsimonious account of business cycle properties that emerges from a smooth DE model with signal extraction. An analytical RBC model featuring Smooth DE accounts for overreaction and overconfidence in surveys, as well as three salient properties of the business cycle: (1) asymmetry, (2) countercyclical micro volatility, and (3) countercyclical macro volatility. A negative shock that raises perceived uncertainty increases the over-reaction to both idiosyncratic and aggregate shocks, and deepens the contraction. This rich and novel propagation arises because the intensity of the DE distortion is state-dependent. In the third project, the team focuses on actionable implications. Under smooth DE, the severity of the DE distortion varies in response to the level of uncertainty faced by agents. The team uncovers a novel role for decision makers: by reducing uncertainty, decision makers can now reduce the severity of the DE distortion and thus stabilize agents’ psychological biases. Thus, a redistributive rules that reduce cross-sectional uncertainty could also be beneficial for macroeconomic stabilization. The team studies the novel welfare implications of this belief stabilization This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY The overall goal of this research is to determine how lipid metabolism supports oxidative phosphorylation (OXPHOS) in acute myeloid leukemia (AML) stem cells. Leukemia stem cells (LSC) are responsible for relapse in AML, and a major goal in the field is identifying novel ways to eradicate AML-LSC. Recently, lipids have been identified as essential metabolic substrates for AML-LSC; however, the mechanisms linking lipids and OXPHOS are not known. To address this, I have determined relapse-specific candidate genes using RNA-Sequencing data from a cohort of pediatric AML patients characterized through the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) study. The results suggest that fatty acid oxidation (FAO) is repurposed to drive heme biosynthesis and fuel OXPHOS in AML relapse. In this K38 StAARTS application, I will test this hypothesis by studying lipid metabolism and OXPHOS in vitro with AML cell lines and in vivo using pediatric patient-derived xenografts. Aim 1 will use stable isotope metabolite tracing experiments to determine if palmitate is a key carbon source for heme in AML cells with LSC properties. Then, Aim 2 will test the effects of a high-fat diet on the growth and aggressiveness of pediatric AML-LSC xenografts obtained through St. Jude’s Public Resource Of Patient- derived and Expanded Leukemias (PROPEL) program. AML-LSC from Aim 2 will also be tested for the effects of a high-fat diet on OXPHOS activity and heme-containing protein content using comprehensive proteomic profiling through the Duke Molecular Physiology Institute’s Metabolomics and Proteomics Core. The results from these studies will provide a mechanistic link between lipid metabolism and OXPHOS in AML-LSC and have the potential to identify new therapeutic targets. My Mentor team, Institutional Environment and Career Development Plan, together with my clinical Hematology-Oncology fellowship, will provide a superb training experience that will ensure the success of this research and prepare me for a future transition to independence as a Pediatric Hematology-Oncology physician scientist.

NIH Research Projects · FY 2024 · 2024-09

Late language emergence (late talking), affects one in five children in the United States today. Therefore, a question pediatricians often face at the 24-month well child visit is which late talking children will require specific assessment and early intervention services to improve their language abilities and long-term outcomes. To connect the right child with the right early intervention there is an urgent need to identify specific developmental trajectories associated with late talking. To date, late talking research has mostly relied on data from relatively small cohort and intervention studies, and included participants who may not be representative of broader pediatric populations growing up in America today. The use of real world data from electronic health records (EHR) offer a unique opportunity to address these gaps by studying large, representative cohorts of children, over longer periods of time to better understand distinct late talking developmental trajectories. While EHR data has been used to study neurodevelopmental and neurological conditions associated with late language emergence, only two EHR studies have specifically focused on late talking. These studies have relied on ICD diagnostic codes to identify the late talking phenotype in EHR records, an approach with inherent weaknesses. Disparities related to child sex, race, ethnicity, primary home language, and insurance status may result in delayed capture of ICD diagnostic codes in the medical record. Furthermore, delays may occur between when parents’ first share concerns with a professional about their child’s development and documentation via an ICD diagnosis of developmental conditions, including late talking. This study aims to improve how EHR data are used to study late talking by employing novel machine learning approaches (natural language processing) to identify late talkers within EHR databases, create open and shared data resources to identify late talking children within EHR, and leverage inherent advantages of EHR data to delineate late talking developmental trajectories. Our experienced research team of psychiatrists, language experts, pediatricians, informaticists, and data scientists are well positioned to achieve our study aims. Our long-term goal is to delineate distant trajectories associated with late talking, thus enabling a personalized intervention approach to improve child outcomes. In follow-up work, we will seek to develop a multi-site consortia to study and intervene with late talkers in real world environments. This will ultimately improve clinical decision-making and referral practices for late talking children.

NSF Awards · FY 2024 · 2024-09

Evolutionary adaptation often involves “fitness tradeoffs” where optimization of one trait comes at the expense of another. For example, a tradeoff between the ability to survive starvation and growth rate upon recovery has been documented in a variety of organisms. However, the molecular mechanisms that underly such tradeoffs are hardly understood despite being fundamental to evolutionary adaption. The roundworm C. elegans is a powerful model system that experiences feast or famine in the wild, and these worms display a tradeoff between starvation survival and recovery rate. The research team has developed an innovative approach using DNA sequencing of mixed populations to measure the fitness of competing strains during starvation and recovery. They have also developed imaging tools to precisely measure biochemical properties of these worms during starvation and recovery. These approaches will enable them to identify strains that are most fit in different evolutionary scenarios and to characterize the molecular mechanisms that support fitness. Each member of the team will engage in outreach. Dr. Baugh will host high school students in his lab during the summer as part of Duke’s Cell Biology Academy (CeBA), and he will visit Dr. Towbin to learn innovative pedagogical methods from the Pestallozzi School Camps (PSCs). The PSCs have a similar mission to the CeBA but have been in operation much longer, and cross-participation will foster the exchange of ideas and approaches. This work will address a fundamental problem in evolutionary biology, advance state-of-the-art approaches, and engage students in research. Evolutionary adaptation to different niches is shaped by phenotypic tradeoffs between life-history traits. Yet the proximal molecular mechanisms of life-history tradeoffs are unknown. The team proposes using the nematode C. elegans to address this knowledge gap and determine the molecular basis of a life-history tradeoff between growth and survival. C. elegans is a powerful animal model to address the molecular mechanisms of trait evolution given its genetic tractability, well-characterized development, short lifecycle, and the availability of hundreds of genetically diverse wild strains with sequenced genomes. The central hypothesis of this proposal is that the frequency and duration of larval starvation in a niche shapes the evolution of a phenotypic tradeoff between starvation survival and recovery speed, and that molecular rates of autophagy/ribophagy impact the balance between the two. This hypothesis will be tested using experimental evolution, genetics, proteomics, quantitative live imaging, and mathematical modeling. The goals of this proposal are to identify wild strains with different life-history strategies in response to starvation, evaluate the mechanistic contribution of autophagy and ribophagy rates to natural variation in starvation survival and recovery speed, and to identify genetic variants that influence the tradeoff between survival and recovery and the molecular mechanism affected. Accomplishment of these goals will be impactful by illustrating the importance of a particular tradeoff to evolutionary adaptation to different niches and by linking adaptation to a particular, conserved molecular mechanism. This collaborative U.S.-Swiss project is supported by the U.S. National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the U.S. investigator and SNSF funds the partners in Switzerland. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2025 · 2024-09

ABSTRACT Early-stage heart failure (HF), defined broadly as asymptomatic structural heart disease, is a growing public health concern and has been receiving greater attention in clinical settings. Early-stage HF is a risk factor for advanced HF, but there is heterogeneity in this risk. While there are known biomarkers and biological mechanisms of advanced HF, little is known about the potential entity of early-stage HF that is distinct from underlying HF risk factors. Additionally, individual -omics platforms have been applied to discover novel biology and biomarkers of advanced HF, but multi-omics data integration methods have not yet been applied in this space. Through preliminary analyses, I have discovered novel proteins for early-stage HF that are distinct from underlying risk factors and advanced HF. With these initial results, I hypothesize that molecular features of cardiac inflammation are significant biomarkers for early-stage HF, and that we can optimize biomarker and biological mechanism discovery by applying integrative -omics techniques. The broad objective of this proposal is to discover novel biomarkers and biological mechanisms underpinning early-stage HF that have prognostic capabilities for advanced HF. Understanding how early-stage HF is distinct from underlying risk factors and advanced HF at a molecular and clinical level could enable the development of novel diagnostic tests and preemptive treatment measures to forestall adverse late-stage outcomes. I propose to accomplish this by utilizing state-of-the-art machine learning methodologies for the analysis of multi-omics and clinical data from large-scale population cohorts. First, I will develop and validate an automated computable phenotype for the identification of early-stage HF using clinical data and imaging-derived measures of cardiac structure and function. I will then apply this computable phenotype in the UK Biobank to identify significant protein and metabolite features of predicted early-stage HF cases using separate single-omics analyses. After identifying candidate biomarkers, I will assess their causality and biological relevance by performing Mendelian Randomization and pathway enrichment analysis. To test the impact of data integration on model predictive ability, I will apply a modified version of MiNet, an interpretable pathway-associated deep neural network for diseas prediction. Using MiNet, I will integrate proteomics and metabolomics data to predict Stage B HF cases versus Stage A HF controls and compare the neural network’s performance to those from the single-omics models. From the trained deep learning model, I will compare pathway node activation in the neural network to the single-omics pathway analysis results to assess whether multi-omics integration affects the richness of biological findings from a model. With this work, I will improve knowledge of early-stage heart failure biomarkers and biology; I will also expand upon the application of evolving machine learning strategies for integrating multi-modal data in the context of complex disease.

NIH Research Projects · FY 2025 · 2024-09

This K22 proposal aims to enhance the applicant’s prior quantitative training through a combination of formal coursework and informal mentorship, supporting their transition to independence as a molecular cancer epidemiologist investigating biological mechanisms underlying disparities in female cancers. Ovarian cancer (OC), the deadliest gynecological malignancy, exhibits marked differences in outcomes that are not fully explained by clinical or socioeconomic factors. Chronic stress—an embodiment of adverse social conditions—has emerged as a potential contributor to these disparities through its influence on biological systems, including the vaginal microbiome. The vaginal microbiome can shape the tumor microenvironment via production of pro-carcinogenic metabolites and reduction of antineoplastic metabolites. Its composition and function are influenced by host factors and social determinants, including chronic stress exposure. Despite growing evidence linking microbiome-related inflammation to cancer progression, no studies have evaluated how chronic stress may alter vaginal fluid metabolite profiles and metabolic signatures in OC patients, nor how these changes may relate to disease aggressiveness and recurrence. Guided by ecosocial theory, which posits that adverse living and working conditions are literally biologically incorporated leaving bodily marks, this study will apply an untargeted metabolomics approach to analyze cervicovaginal fluid from 120 OC patients, with detailed assessment of chronic stress exposure at both individual and neighborhood levels. The specific aims are to: i). Characterize vaginal fluid metabolite profiles and metabolic signatures among OC patients, ii). Assess differences by chronic stress in vaginal fluid metabolite profiles and metabolic signatures among OC patients, and iii). Evaluate the relative importance of vaginal fluid metabolites and chronic stress on OC aggressiveness and recurrence. Completion of the proposed K22 will advance scientific understanding of how chronic stress and vaginal microbiome-derived metabolites contribute to OC progression. This knowledge may inform the development of prognostic biomarkers and therapeutic strategies targeting microbiome-mediated pathways. Clinically, the study may support the integration of vaginal metabolite profiling into personalized care approaches for OC patients. The proposed training and research will equip the applicant with the skills and resources necessary to establish an independent research program in molecular cancer epidemiology.