Duke University

NSF Awards · FY 2025 · 2025-10

Large Language Models (LLMs) are increasingly deployed as the backbone of real-world applications such as Google Search with AI Overviews and Microsoft Bing Copilot. When data and code are not properly separated within an application, the latter (including AI applications) is vulnerable to cyber-attacks. This project's novelties are twofold: (1) conducting a systematic study to deepen the understanding of such threats, and (2) developing new defenses to mitigate such attacks. Its broader significance and importance lie in establishing foundational security principles for the rapidly growing ecosystem of AI applications, which are now widely deployed across diverse societal domains. Moreover, the released code and materials produced by this project will not only help secure real-world LLM-integrated applications but also serve as valuable educational resources for undergraduate and graduate courses, fostering the next generation of researchers and practitioners in this emerging security area. Security history shows that when data and instructions are not properly separated within a system, injection attacks can emerge—for example, SQL injection attacks in traditional software. Similarly, due to the lack of a clear boundary between instructions and data in prompts, LLM-integrated applications are inherently vulnerable to prompt injection attacks. To understand and mitigate such threats this project adopts a holistic approach comprising three interconnected research thrusts to systematically investigate the security vulnerabilities of LLM-integrated applications to prompt injection attacks and to develop new methods to prevent, detect, and attribute such attacks. The project will also open-source a platform that integrates our developed algorithms along with a comprehensive tutorial on prompt injection attacks and defenses. 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 2026 · 2025-09

Abstract: Millions of Americans are current or past cocaine users, with 2% of the US population reporting to have used the drug in the past year. Cocaine is highly addictive, and cocaine addiction is devastating to society and individuals, leading to financial loss, job loss and violence within families. Additionally, cocaine abuse is responsible for 40% of drug-related emergency room visits and 1 in every 5 overdoses. Cocaine acts as a monoamine reuptake inhibitor, preventing the dopamine from being transported back into cells after its synaptic release, which leads to prolonged and enhanced dopamine action. Cocaine use especially affects the limbic system, in particular, nucleus accumbens, as well as hippocampus, the amygdala and the prefrontal cortex, leading to changes in activity and long-term rewiring of the circuitry, which causes cocaine craving and addiction. Previous studies identified the major brain regions associated with cocaine addiction, as well as demonstrating that long-term cell- intrinsic molecular changes, such as in epigenetic signaling, underlie the establishment of cocaine addiction. However, the knowledge of how cell type-specific molecular changes resulting from cocaine use lead to rewiring of circuits relevant to cocaine-seeking behavior is currently limited. Moreover, treatments of cocaine addiction that prevent drug seeking via restoring the normal states of neurons and circuits that are dysregulated as the result of repeated cocaine use are desperately needed. We will utilize mice repeatedly exposed to cocaine in a well-established conditioned place preference paradigm to investigate cell type and circuit-specific epigenetic mechanisms underlying cocaine addiction. Using a combination of a novel approach we developed called inducible barcoded rabies virus with activity-dependent neuronal tagging and single-cell genomics, we propose to identify epigenetic changes in specific limbic system neuronal cell types and circuits active during cocaine- seeking behavior. We will then test the hypothesis that cocaine-seeking behavior can be erased by performing epigenetic editing of hippocampal neurons projecting to the nucleus accumbens that are active during detection of cocaine-conditioned cues. To that end, we will utilize a novel gene editing tool we developed called CRISRP- rabies to modulate epigenetic states of specific neuron types within defined hippocampal-NAc circuits conditioned by cocaine use. We believe that our study will discover conceptually novel mechanisms of cocaine addiction and will build a foundation for new types of therapies of drug addiction by rewiring specific circuits responsible for drug seeking behavior.

NIH Research Projects · FY 2025 · 2025-09

This proposal seeks support for students and junior investigators (travel awardees) to attend the 2025 International Society for Eye Research (ISER)/BrightFocus Foundation (BFF) glaucoma meeting titled "Concepts and Breakthroughs in Glaucoma" to be held October 8th-11th, 2025 at the Emory Conference Center and Hotel in in Atlanta, Georgia. As in past meetings, our goal is to bring together basic scientists, clinician-scientists, students and fellows for presentations and in-depth discussions on recent exciting research advances and developments in the molecular mechanisms underlying glaucomatous pathology, both in the conventional outflow tract and the optic nerve head. We have already recruited three thought leaders in glaucoma to deliver keynote lectures. As in past meetings, platform sessions will be selected exclusively from submitted abstracts, with one session reserved for travel awardees. We are also organizing again a one-day "crash course" in glaucoma for people newcomers to the field and students/junior investigators, consistent with our goal of increasing young scientist participation. The Specific Aims of the conference are to: 1. Enhance the emerging careers of at least 30 young investigators working in glaucoma research by providing travel awards. 2. Provide a forum for the dissemination of the most recent advances in glaucoma research. 3. Create an environment that facilitates the exchange of novel ideas among basic and clinician-scientists, fostering opportunities for collaboration among vision scientists with multiple scientific expertise. 4. Bring together scientists working in disparate areas of glaucoma research. We anticipate that this meeting will provide state-of-the-art information on recent advances in glaucoma and serve as an important resource for those involved in the translation of these findings into novel therapeutics. The conference will also provide new opportunities, avenues for new discovery, and a forum to develop potential collaborations among the attendees. The requested funds will support the travel, accommodation and registration of at least 30 trainees to attend this focused meeting.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT The opioid epidemic is a primary driver of new hepatitis C virus (HCV) infections, especially among younger (<40 years) people who inject drugs (PWID), including new outbreaks. PWID are also at risk of human immunodeficiency virus (HIV) infection and therefore a key population for engagement in preventative interventions, including pre-exposure prophylaxis (PrEP). The United States has proposed integrated models of care to address these syndemic conditions. In PWID, HCV transmission mostly occurs in the first two years after drug injection initation, while for HIV it occurs after 6-8 years, providing support for new HCV infections outpacing new HIV infections in this key population. Jumpstarting and integrating diagnosis, treatment, and prevention for HIV and viral hepatitis in the US is critical to address these overlapping epidemics. The magnitude of HCV infection in the US and our inability to meet global (World Health Organizatioin, WHO) and US (Center for Disease Control and Prevention, CDC) goals requires innovations in how care for HCV and HIV is delivered, which will be addressed in this application. Bringing together an international consortium with broad and relevant expertise, we propose a clinical trial to demonstrate the feasibility, acceptability, and clinical effectiveness of a single-visit POC test & treat integrated care model approach addressing roadblocks in the HCV care continuum and integration of HIV prevention and treatment linkage services. The results of this landmark trial could deliver a paradigm shift in the approach to HCV care in the US and inform strategies for HCV elimination globally. The overall objective of the proposed research is to directly address the 3 key priorities of the US National HCV Elimination Program, which include: (1) expansion of POC diagnostic testing; (2) broad access to curative HCV medications; and (3) efforts to engage, inform, identify, and treat people with HCV infection. With availability of POC tests for HCV, HIV, and HBV infections, novel integrated care models, as proposed in this application, can be developed and tested for overcoming barriers to prevention and treatment of HIV and HCV. Our long-term goal is to contribute to the elimination of HCV infection in the US. To achieve this goal we propose the following specific aims: 1) Evaluate the effectiveness of a single-visit point-of-care test & treat bundle on outcomes relating to HCV and HIV among key populations, including people with or at risk for HIV, 2) To assess the acceptability, barriers, and facilitators of an integrated POC HCV test & treat care model for participants, healthcare providers, and testing operators, and 3) Evaluate the cost-effectiveness, affordability, and long-term epidemiological impact of a scaled-up single-visit test & treat care model among key populations, including people with or at risk for HIV.

NIH Research Projects · FY 2025 · 2025-09

Hypertensive disorders of pregnancy (HDP) are one of the most common pregnancy complications, and a leading cause of perinatal mortality and morbidity. People with HDP are at increased risk of life-threatening pregnancy complications and postnatal cardiovascular disease, stroke, and type 2 diabetes. In the US, prevalence of HDP has increased over time and exhibits stark health disparities; neither trend is explained by known risk factors. Environmental exposures, particularly those that disrupt endothelial and placental function, may contribute to HDP risk. A developing body of work examines whether ubiquitous, modifiable environmental exposures such as air pollution, temperature extremes, and lack of greenness – all biologically plausible and potentially modifiable – relate to HDP risk. However, studies are limited to HDP only, not repeated measures of blood pressure (BP trajectories), use pre-specified time windows of exposure, and have reported mixed results. For a common condition with rising incidence and profound short- and long-term health consequences, the links between environmental exposures, HDP, and BP are understudied. Utilizing electronic health records (EHR) from health systems in Durham, NC; Chicago, IL; and New York, NY, we link EHR of >320,000 individuals across pre-pregnancy, prenatal, and post-partum periods (2010-2019) with finely-resolved environmental exposure data. Our overarching objective is to understand if and how air pollutants, temperature extremes, and lack of greenness are associated with HDP in pregnancy and BP trajectories. We pursue three aims: (1) Model spatial and temporal trends in neighborhood-level HDP rates and health disparities in HDP to identify high risk neighborhoods; (2) Evaluate flexible time windows of association between multiple environmental exposures, HDP risk, and health disparities in HDP risk; and (3) Investigate relationships between multiple environmental exposures, BP trajectories (including post-pregnancy) and health disparities in BP trajectories. Our innovative, Bayesian spatiotemporal modeling approach and individual-level, longitudinal data from three regions of the US allow us to assess consistency and generalizability of findings across different samples of women with varying exposure mixtures. The proposed research will transform our understanding of which environmental exposures are most strongly associated with HDP and BP trajectories, and the extent to which the environment affects health disparities. As the country strives to improve maternal health, our findings will inform the type, timing, and tailoring of interventions to improve health.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Glioblastoma (GBM) is the most common primary malignant brain tumor in adults and remains uniformly lethal, with median survival <15 months despite intensive therapy. High-dose ionizing radiation remains the only confirmed environmental risk factor, but the first stages of tumor initiation appear to occur decades prior to clinical diagnosis. Although the etiology of GBM remains poorly understood, genome-wide association studies (GWAS) have identified seventeen independent risk alleles associated with heritable predisposition. These alleles are non-coding regulatory variants, and their impact on disease risk reflects the varied gene expression patterns and epigenetic programs of the underlying cell types and states involved in tumor initiation and maintenance. We recently performed stratified Linkage Disequilibrium Score Regression (SLDSR) analysis to partition the heritability of GBM across tissue types, observing significant enrichment in deep gray matter structures of the brain and in lymphocytes at the interface of innate and adaptive immunity. However, SLDSR is limited by the underlying reference datasets employed. We hypothesize that a subset of cell states/types comprising the tumor, the tumor microenvironment, and the developing brain will be enriched for GBM heritability, reflecting their critical roles in cellular transformation and tumor establishment. Our OVERALL GOAL is to incorporate single-cell transcriptomic and epigenomic datasets into an improved platform for partitioning GBM heritability at unprecedented resolution, revealing both early neurodevelopmental and late pathologic cell types that serve as effectors of GBM predisposition. This collaborative project will leverage a wealth of existing data resources to test our hypothesis through three complementary aims. In Aim 1, we will partition the genetic heritability of GBM across meta-modules of malignant cell states and microenvironmental cell types generated from harmonized snRNA-seq and snATAC-seq datasets in order to identify the cell populations through which genetic predisposition precipitates GBM formation. In Aim 2, we will overcome the lack of ancestral diversity in GBM profiling data by performing multiome snRNA-seq and snATAC-seq of GBM cells from 24 African American patients, evaluate the impact of ancestry on meta-module construction, and partition GBM heritability across cell states/types. In Aim 3, we will use existing snRNA-seq and snATAC-seq datasets from fetal brain tissues to partition GBM heritability across early cell lineages in order to examine the neurodevelopmental origins of GBM. Novel cell states and tumor cell subpopulations enriched for GBM heritability in these aims will be further characterized and validated using spatially-resolved, in situ RNA sequencing of patient tumors. Despite extensive molecular and epidemiologic research, the origin of GBM remains poorly defined. This proposal represents an innovative approach for integrating existing GWAS and single-cell datasets to reveal potentially unanticipated cell populations involved in GBM formation. Such cell populations may serve as targets for GBM interception or for the development of more effectively targeted therapies.

NIH Research Projects · FY 2025 · 2025-09

Abstract Effective treatment of aggressive and treatment-resistant cancers, such as head and neck squamous cell carcinoma (HNSCC), demands innovative methodologies that accurately capture the complexities of the tumor microenvironment and predict therapeutic responses. However, traditional imaging techniques alone may not fully capture the complex and dynamic processes of tumor growth, angiogenesis, and therapeutic responses, limiting our ability to develop and optimize effective treatments. This project aims to develop a Virtual Preclinical CT (VPCT) platform to serve as a "digital twin" for studying preclinical cancer models of HNSCC. By integrating advanced photon-counting CT (PCCT) imaging with sophisticated computational models, the VPCT platform will simulate tumor behavior, optimize imaging protocols, and evaluate therapeutic interventions in a virtual setting. The VPCT platform will combine high-resolution tumor and vascular models with PCCT simulations to accurately replicate the anatomical and physiological complexity of mouse cancer models, thereby enabling precise simulations of therapeutic outcomes. Our approach is organized into three specific aims: Aim 1 focuses on developing the VPCT imaging platform by enhancing digital mouse phantoms with detailed vascular and tumor models derived from high-resolution PCCT data and integrating multiscale tumor models using computational platforms like CompuCell3D. Aim 2 will validate the VPCT platform by comparing simulated imaging data with empirical data from custom-designed phantoms and in vivo PCCT imaging of HNSCC tumors, ensuring the platform’s accuracy in material separation and vascular mapping. Aim 3 will leverage the validated VPCT platform to optimize and evaluate radiation therapy (RT) and combination treatments for HNSCC. Specifically, we will identify the optimal timing for injecting high atomic number (Z) barium nanoparticles (VivoVist™) in relation to RT to maximize vascular disruption and enhance the enhanced permeability and retention (EPR) effect. Additionally, we will explore the synergistic potential of combining VivoVist™ nanoparticles with RT and the chemotherapy agent Doxil (liposomal doxorubicin). By integrating advanced radiomics, machine learning, and computational pathology, we aim to understand therapeutic strategies and significantly improve the accuracy of treatment outcome predictions. The VPCT platform is anticipated to significantly improve tumor modeling and optimize PCCT protocols for assessing the effects of RT on tumor vasculature and evaluating combination therapies. By focusing on vascular changes and the synergistic effects of combined treatments, the platform will offer critical insights for refining RT and chemotherapeutic strategies in cancer treatment. Additionally, integrating advanced computational techniques will enhance predictive accuracy, enabling preclinical findings to directly inform clinical applications and advance personalized cancer therapies.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY This K01 Career Development Award will inform the design and development of an R01-level intervention utilizing health coaching and precision-level strategies to cultivate PA maintenance as women transition from CR into the real world. Cardiovascular disease is the leading cause of death for women in the United States. Cardiac rehabilitation (CR) can play an important role in attenuating cardiovascular disease progression, reducing morbidity and mortality, and improving quality of life. Unfortunately, CR programs have limited success in producing sustained behavior change beyond the structured program, especially among women, mitigating long-term health benefits. Given women appear to participate in less PA during and following CR, there is a critical need to understand whether they have different barriers and thus different predictors of PA participation and maintenance. Prior research investigating barriers and predictors of PA participation during and after CR has been limited by the study population, which has been predominately male. Thus, the proposed project will utilize an observational study design to assess the following aims: 1) among individuals enrolled in CR, identify sex differences in PA participation during CR and PA maintenance after CR completion; 2) identify sex differences in patient-level barriers to PA participation during CR and PA maintenance after CR completion; 3) identify key predictors of PA participation during CR and PA maintenance after CR; and Sub-Aim 3a) determine whether identified predictors of PA participation and maintenance differ by sex. My central hypothesis is that 1) sex differences in PA participation, barriers to, and predictors of PA during and following CR will be identified; and 2) qualitative experiences of men and women will provide a mechanism to tailor a PA transition program to better support women in improving cardiovascular health and CR recurrence. The proposed research plan will advance the applicant’s career, Katherine A. Collins, PhD facilitating her transition to independent investigator status over the five years of K01 support. A team of highly productive, multidisciplinary scientists (Drs. William Kraus, Jennifer Gierisch, and Valerie Smith) will collectively serve as mentors to Dr. Collins during the proposed research at Duke University. This team of mentors has the expertise to train Dr. Collins in 1) qualitative and mixed methods research; 2) predictive analytics, longitudinal data analysis, and sex differences; and 3) grantsmanship and professional development. The proposed training and expert mentoring team will provide Dr. Collins with the skillset, preliminary data necessary to compete for R01 funding, and protected time to establish an independent laboratory. In sum, this award will serve as a catalyst for making Dr. Collins a leader in the field of precision lifestyle and exercise medicine.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Osteoarthritis (OA) is a total joint disease characterized by articular cartilage degradation, synovial inflammation, meniscus degeneration, subchondral bone sclerosis, osteophyte formation, and joint pain. It afflicts nearly 1/4 of the US population resulting in healthcare expenditures exceeding $185 billion annually. Despite the severity and impact of OA on individuals and our health care system, only recently have there been advances in understanding the molecular, cellular and tissue events underlying OA development and progression. It is well established that inappropriate expression and activation of catabolic enzymes underlies the joint cartilage destruction observed in OA, however the precise molecular mechanisms responsible for promoting joint cartilage catabolism is not well understood, nor is there a defined understanding of the molecular mediators of OA- associated pain. Recently, we determined that IL-6/JAK signaling is a critical mediator of both cartilage catabolism and pain in post-traumatic OA (PTOA) of male mice, and that particular JAK signaling molecules likely mediate PTOA-associated cartilage catabolism and pain. Here we will explore the relevant downstream mechanisms and test specific pharmacological inhibitors of these pathways as disease modifying osteoarthritis drugs (DMOADs). Using cartilage-specific and nociceptive neuron-specific inducible loss-of-function genetic approaches, we will identify the Janus Kinases (JAK1, JAK2, and/or TYK2) that mediate both/either the cartilage specific catabolic effects and the nociceptive pain responses associated with PTOA. A variety of in vivo and ex vivo approaches will identify the particular JAKs and critical downstream effectors as important regulators of PTOA-associated cartilage catabolism and pain, while simultaneously testing selective JAK inhibitors as potential translational DMOAD therapies.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT This funding opportunity from the NIH HEAL Initiative calls for an authentic precision health approach to understanding and addressing pain and pain experiences. Sickle cell disease (SCD) is a historic, global, classic Mendelian disease with extensive phenotype variability attributable to diverse modifiers of disease severity. The hallmark of SCD is pain, which is multifaceted and woefully undermanaged. Pain is also the primary cause of hospitalization in patients with SCD and is correlated with increased morbidity, mortality, and healthcare costs, contributing to health disparities. Despite considerable research on SCD pain, with many singular associations, the findings have been largely disparate and tenuous. No studies have systematically modeled the SCD disease process or SCD pain as networks of relationships across multiple levels of influence. To improve pain assessment and management for SCD patients, we need a better understanding of the acute to chronic pain spectrum, how pain experiences unfold over time, and how individuals are affected by their pain state. The goal of this project is to develop and validate novel computational and clinical tools for understanding and addressing differences in pain experience and response to pain treatment among people with SCD. To that end, we will utilize the Globin Research Network for Data and Discovery (GRNDaD), a multisite SCD registry, to conduct a prospective cohort study of 1250 individuals (≥15 years of age) living with SCD in the US. Across three timepoints during a 3-year period, we will obtain representative measurements of the pain experience as well as known and putative exacerbators and ameliorators of acute and chronic SCD pain within seven domains (biological, clinical, behavioral, psychological, environmental, sociocultural, and structural), including the required NIH HEAL Common Data Elements (CDEs) and comorbidities such as depression, asthma, and substance use/misuse. This project will have three Aims: Aim 1: Create a set of Baseline Pain Profiles (BPPs) for SCD; Aim 2: Construct Precision Pain Profiles (PPPs) and compare them with the BPPs; and Aim 3: Develop Precision Models and Mechanisms (PMMs) that integrate diverse data types and explain the PPPs. The scope and expected deliverables of this project are unprecedented. This will be one of the most comprehensive investigations of the individual and collective influences of proximal and distal factors on SCD outcomes. Through this whole person approach, the study will generate new knowledge about the intricacies of pain; produce innovative statistical and computational methods and tools for SCD research and clinical care; and catalyze the development of novel and effective prevention, intervention, and treatment strategies that could help bring an end to the national opioid health crisis. What we learn about mitigating and managing SCD pain and pain in general will be translatable to other diseases and conditions globally.

NSF Awards · FY 2025 · 2025-09

Computers, be it those in mobile phones or servers, are increasingly being designed with heterogeneous processors and memory that is shared across all of these processors. The heterogeneity enables greater performance, with different processors tailored for specific purposes (e.g., graphics), and the shared memory facilitates easier programming. These processors are already being used to support critical computational tasks, including artificial intelligence (AI), robotics, and medical research. While these processors offer great potential, they pose two problems. First, it is difficult to understand how to compose them. Specifically, different types of processors use different communication protocols, and composing these protocols is complicated. Second, it is also challenging to verify that the processors will behave correctly in all situations. The composed protocols have a vast number of possible interactions, and verification techniques do not scale up to meet this challenge. This project addresses both challenges by developing a systematic way to compose processor protocols and a new, scalable technique for verification of these processors. These contributions can offer many benefits, including shorter time to market, confidence that processors will behave as expected, and a lower barrier to entry for startups and researchers seeking to create new processors. By providing a foundation for the correct design of heterogeneous shared memory processors, the project will help to enable the coming generation of high-performance computing systems. These systems will sustain American economic competitiveness, supporting breakthroughs in AI, medicine, science, defense, and many other fields that will enhance the lives of all Americans. This project will make three important contributions to the theory and practice of processor design and verification. First, it will provide, for the first time, a mathematical foundation for defining and reasoning about the interaction of programs sharing memory in a heterogeneous system. This understanding will be crucial for designing the coming wave of heterogeneous systems-on-chip that will drive system performance for consumers and industry in the era of AI. Second, the work will provide an understanding of the large design space of heterogeneous coherence protocols and the first automated tools for correctly synthesizing the protocol converters needed to connect diverse local and global protocols. Third, the project will develop the first compositional approach for verifying heterogeneous coherence protocols and the first application of translation validation to cache coherence protocols. It will integrate verification as part of the protocol design flow, enabling designers to realize cost-effective proofs, and provide an exemplar for making formal methods practical in systems design. 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 2025 · 2025-09

The United States faces critical challenges in coordinating federal and state government efforts to address rapidly evolving technological and workforce needs, particularly in artificial intelligence and workforce development. Despite extensive federal funding flowing to states, meaningful collaboration between these levels of government remains limited, hindering America's ability to effectively deploy resources for innovation, economic development, and workforce preparation. This project addresses this fundamental gap by creating structured pathways for federal-state partnerships that will enhance the nation's capacity to respond to technological change, strengthen regional innovation ecosystems, and ensure that federal investments in science and technology are optimally leveraged at the state level. By focusing on artificial intelligence and workforce development, an area of critical national importance, this initiative will demonstrate how enhanced federal-state collaboration can accelerate technology transfer, improve government services, and prepare American workers for the jobs of tomorrow, thereby advancing national prosperity and maintaining U.S. competitiveness in the global innovation economy. This project will employ a phased approach beginning with virtual listening sessions to identify key barriers and opportunities in federal-state partnerships, followed by a leadership summit in Washington, DC that will bring together representatives from NSF, other federal agencies, and approximately 20 states to design pilot partnership programs. Through facilitated workshops focused on AI applications in workforce development, participants will co-create actionable pilot programs that demonstrate new models for sustained collaboration, including joint technology transfer initiatives, shared data platforms, and coordinated workforce training programs. Following the summit, the project will establish an 18-month community of practice to further mature these pilot programs in accelerating partnership formation in workforce readiness and technology deployment. The initiative will produce a replicable framework for federal-state partnerships that can be adapted to other critical areas beyond AI and workforce development, fundamentally transforming how different levels of government collaborate to address complex national challenges. 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 2026 · 2025-09

Project Summary Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by memory loss and cognitive impairment which constitutes the most common type of dementia and impacts nearly 6 million people in the US. AD has a complex etiology which includes genetic contributions, environmental factors, and age- associated changes in the brain. In addition, brain inflammation has emerged as a key factor in the development and progression of AD. Thus, understanding the brain’s inflammatory response is critical for comprehending the factors that contribute to AD as well as for identifying biomarkers and novel therapeutic targets. Within the brain, microglial cells are the major cell type that mediate the response to neuroinflammation. These cells normally exist in a resting state but then become activated in response to inflammatory stimuli. At early stages of disease, microglial activation plays a beneficial role by releasing anti-inflammatory cytokines and contributing to the clearance of pathogenic protein aggregates that accumulate in the AD brain. However, prolonged microglial activation causes the release of neurotoxic factors and excessive synaptic pruning, promoting disease progression. Thus, understanding the factors that control the transition from a resting to an active microglial state and elucidating how microglial function is regulated during AD pathogenesis is crucial for determining how this critical cell type contributes to disease, as well as for developing novel microglial-targeting therapeutics. One mechanism that the brain uses to control gene expression is methylation of adenosine residues in mRNA to form m6A. The m6A modification is found in thousands of transcripts and enables fine-tuning of gene expression in response to a variety of stimuli and cellular states. In addition, m6A and its regulatory proteins are altered in the brains of AD patients. Here, we will explore the hypothesis that dynamic regulation of m6A contributes to gene expression changes that underlie microglial cell activation in response to inflammation. Using new technologies developed in our lab, we will identify for the first time the m6A sites that undergo dynamic methylation in microglia during AD progression (Aim 1). Then, we will uncover the proteins in microglia that bind to m6A and investigate how microglial activation alters RNA:protein interactions (Aim 2). Finally, we will determine how microglial- specific depletion of m6A contributes to microglial function and AD progression using a novel mouse model (Aim 3). Altogether, these studies will be the first to explore m6A dynamics in microglial cells in vivo during AD and will provide new knowledge of the role of m6A in microglial activation and AD progression.

NIH Research Projects · FY 2025 · 2025-09

MoTrPAC DXA Supplement - Project Summary The Molecular Transducers of Physical Activity Consortium (MoTrPAC) is designed to discover and characterize the range of molecular transducers underlying the effects of exercise in humans. MoTrPAC was launched in 2016 with six adult clinical centers and a pediatric center that have collaborated to generate extensive Manual of Operations to guide research protocols involving all aspects of the clinical operations (Phase I). Phase II began in the fall of 2019 with all human clinical centers showing excellent progress towards initial recruitment goals and implementation of the protocol. The initial goal set forth by NIH was to recruit 270 children (10-17 years of age) and 1980 sedentary adults (age 18 years or greater) randomized to endurance training (170 youth, 840 adults), resistance training (840 adults), or non-exercise controls (50 youth, 300 adults). An additional group of highly active endurance (50 youth, 150 adults) and resistance (150 adults) trained individuals serve as comparators, not participating in the MoTrPAC exercise training programs. The recruitment and enrollment approach are sex-balanced, with participants across a wide range of ages (10-17, 18-39, 40-59 and >60-year age groups) and of different races. Due to the COVID-19 pandemic, beginning in March 2020, MoTrPAC activities were suspended for over a year with continued constraints through 2022. Despite the numerous challenges encountered as a result of the pandemic, the human clinical centers have successfully enrolled and completed the adult and pediatric highly active cohorts, the pediatric cross-sectional cohort, and the previously sedentary adult and low-active pediatric randomized control trial portion of the study as of May 2025. As data quality control is underway, discrepancies in dual-energy X-ray absorptiometry (DXA) data have been detected across different machines. This led NIAMS to provide MoTrPAC with funding to conduct additional DXA scans among previously enrolled MoTrPAC participants to develop a correction equation accounting for differences in DXA machines, allowing all MoTrPAC DXA data to be included in the final dataset. Altogether, this will allow MoTrPAC to complete the intended goals as originally envisioned, will provide a more complete public database of the health benefits of exercise, and will provide insight into how physical activity mitigates disease risk.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Post-tuberculosis lung disease (PTLD) is a major complication of pulmonary tuberculosis (TB), contributing to significant impairment and early death among TB survivors in sub-Saharan Africa. Treatment for PTLD is complicated by the wide heterogeneity in disease patterns which exist. One treatment which reduces PTLD morbidity across clinical patterns is pulmonary rehabilitation (PR). PR consists of a combination of aerobic endurance training, strength and resistance exercises, structured education, and behavior change, each of which can be personalized according to individual characteristics and clinical patterns of lung disease. Yet, PR is often unavailable in high-TB burden settings due to its resource-intensive and multi-component nature. In order to make PR for PTLD more widely available in a way that is sustainable in resource-limited settings, we must 1) define the clinical patterns of PTLD and associated clinical outcomes so we can identify who has the greatest need for PR, 2) develop a structured process for tailoring PR programs for different settings, and 3) establish the feasibility and acceptability of a tailored program in a high-TB burden setting. Dr. Navuluri is a pulmonary-critical care physician with experience in chronic lung disease research in Kenya. She previously found that routinely available clinical data can be used to identify clinical patterns of disease in a setting where tools more commonly used for pattern identification, such as high-resolution computed tomography or complete pulmonary function testing, may not be available. She also found that individuals with PTLD and health system leaders identify a strong need for PR programs and that adapting individual components and aspects of PR program delivery can help address the multiple system-, provider-, and patient-level barriers which exist. To address this need, Dr. Navuluri will use Intervention Mapping for Adaption, a validated, theory- based framework, to complete three specific aims. In Aim 1, she will employ machine learning methods (specifically cluster analyses) to identify clinical patterns of PTLD and their impact on quality of life and functional status using routine clinical data. In Aim 2, she will use human-centered design methods to develop an implementation blueprint for adapting PR for individuals with PTLD across resource-limited settings and create an adapted PR program for use in Kenya. In Aim 3, she will conduct a single-arm pilot study to assess the feasibility, acceptability, adoption and fidelity of an adapted PR program and plan for a future large-scale hybrid implementation and effectiveness trial. Throughout this work, Dr. Navuluri will learn how to apply machine learning to inform implementation, use human-centered design to adapt interventions, and design implementation trials. The proposed studies and training will allow Dr. Navuluri to develop the necessary skillset for a successful, independent career in implementing evidence-based interventions to improve respiratory outcomes in resource-limited settings.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Acute Respiratory Distress Syndrome (ARDS) is a serious lung injury requiring critical and complex care in the Intensive Care Unit (ICU). ARDS results in increased capillary permeability and fluid build-up in the lungs which affects oxygen delivery to red blood cells and leads to non-compliant or "stiff" lungs. Sepsis and trauma remain the major contributors to ARDS among patients admitted to the ICU. Each year, more than 200,000 Americans are diagnosed with ARDS and receive intensive care treatment, resulting in more than 3.6 million hospital days. Mortality rates are extremely high, with an average reported figure of 43% overall, representing a disease deadlier than ischemic heart disease and embolic stroke. There is still no pharmaceutical treatment available for ARDS; however, a number of interventions have been shown to be highly effective in reducing mortality. ARDS continues to be underdiagnosed and missed in up to half of the mild cases. This prevents the judicious and timely applications of effective interventions that have been proven to reduce mortality. Our project seeks to develop and validate predictive models of ARDS using protein biomarkers and clinical data for early diagnosis (Aim 1). Early detection of ARDS using parsimonious biomarkers is needed to improve recognition and timely administration of supporting interventions. We also seek to elucidate resiliency markers and endotypes associated with ARDS recovery using an integrated multiomics pipeline (Aim 2). Underlying biological mechanisms that drive severity and recovery are still unknown. There is now a distinct interest in identifying ‘host-factor resilience’, defined as ability of a host to tolerate the effects of an infection or other significant perturbation and promote salutogenesis. Our main goal is to characterize such protective factors among those who recovery to discover possible therapeutic targets of ARDS. This complex undertaking necessitates a holistic analysis supported by the integration of multiomics sources, such as transcriptomics, metabolomics, proteomics and lipidomics to gain crucial insights into the host-factors that drive resilience and recovery among the critically ill patients.

NSF Awards · FY 2025 · 2025-09

Cardiovascular disease is the leading cause of death globally, yet many diagnostic and treatment decisions rely on costly or invasive procedures. This project develops advanced computer models that act like “virtual replicas” of a patient’s blood vessels and heart activity. Using everyday heart-rate data from wearable sensors, the investigators simulate how blood flows and interacts with vessel walls over weeks or months, all on powerful computers. The investigators also build tools to capture how each person’s vessel shape and branch patterns vary and then create large groups of realistic virtual patients to test devices and therapies without risk. By replacing or reducing the need for invasive tests and accelerating the evaluation of medical devices, this work promises earlier detection of heart problems, more personalized care, and faster delivery of safer treatments to patients. This project addresses the need for scalable, high-fidelity assessment of how anatomical variability influences hemodynamic biomarkers. The investigators will build a computational framework that systematically surveys changes in vascular geometry and quantifies their impact on key fluid‐dynamics‐derived metrics—such as wall shear stress, oscillatory shear index, and pressure gradients. First, the research team will cluster patient‐specific models using a Jaccard‐index similarity metric on discretized centerline geometries to narrow the anatomical search space. Next, representative geometries from each cluster will undergo large‐scale, physics‐based simulations on leadership‐class computing resources. By combining clustering‐driven geometry selection with high‐resolution modeling, the investigators will generate spatial and temporal maps linking morphological variation to biomarker distributions across virtual cohorts. This framework enables in silico trials of vascular interventions and supports the discovery of novel computational phenomarkers for personalized cardiovascular care. 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 · 2025-09

Accurately predicting whether patients will live or die is an essential task in critical care medicine. Yet such predictions are often incorrect based on specific patient characteristics. We and others found that Acute Physiology and Chronic Health Evaluation (APACHE), Sequential Organ Failure Assessment (SOFA), and other prognostic tools overestimate the risk of death for certain groups of patients. Because the incidence of serious cardiac or respiratory critical illnesses is so high, the consequences of this error are significant for clinical decision making, research, and health policy. Given that APACHE II alone has been included in >10,000 studies, an entire medical literature may imperfectly measure the effectiveness of ICU treatments. Furthermore, the use of SOFA in ICU triage policies, such as during the COVID-19 pandemic, may propagate variability in health outcomes by incorrectly withholding life-saving ICU treatments from some patients. There are two key knowledge gaps that limit a solution including a lack of understanding of how measurement errors arise in these prognostic tools and the absence of ICU prognostic tools that are free of such errors. Filling these gaps will promote high-quality ICU care and access to it, as well as ICU research. Our goal is to develop a novel ICU mortality risk prediction model with accurate performance across all patient groups. To mitigate measurement error, we will combine state-of-the-art methods with the necessary supervision and contextual grounding provided by multidisciplinary advisors (critical care, bioethics, data science). Utilizing data from >100,000 patients broadly representative of the US population from 23 ICUs at Duke and Medical University of South Carolina, this project has three aims: (1) Identify mechanisms of systematic measurement error in commonly used ICU mortality risk prediction models; (2) Develop an accurate in-hospital mortality prediction model for all groups of critically ill patients; and (3) Conduct a real-world feasibility study of the new model within our electronic health record. The expected outcomes of this study, which addresses NHLBI’s key objectives to reduce variability in health outcomes and improve care by leveraging the power of data science, are two-fold. First, to our knowledge, this will be the first study to identify mechanisms of measurement error in current ICU mortality risk prediction models and to include variables that reflect the living situation of patients in ICU risk prediction—innovations that will serve as a conceptual foundation for future work. Second, the new model will have an immediate and substantial public health impact by promoting quality and representativeness in clinical care, research, and policy. Our proposal is innovative because it includes novel applications of model variability constraints and metrics; it also includes multidisciplinary advisors to ensure person-centered model development and evaluation. This study is feasible because of our team’s expertise and our rich research and clinical environment—all critical to navigating the complex sociotechnical context in which the proposal exists.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT The Center for Virtual Imaging Trials (CVIT) serves as a premier national resource for in silico imaging research, providing advanced computational phantoms, physics-based imaging simulators, and AI development tools to support hundreds of users across academia, industry, and regulatory agencies. Virtual imaging trials (VITs) enable efficient, rigorous evaluation of imaging technologies and protocols under controlled conditions with known ground truth, offering a powerful complement to traditional clinical trials. Maintaining this capability is critical for advancing diagnostic innovation, regulatory science, and precision medicine. This administrative supplement provides essential gap funding to sustain CVIT operations during a transition period and to launch key advancements aligned with the Center’s original aims. These initiatives, which address high-priority needs identified by the community, represent the first steps toward the next renewal phase and focus on three areas of biologically realistic lesion modeling (a sub-aim from the anticipated TRD1 renewal), integrated PET-CT simulation (a sub-aim from the anticipated TRD2 renewal), and hybrid AI training resources (a sub-aim from the anticipated TRD3 renewal). Collectively, these efforts will sustain critical CVIT infrastructure while delivering tangible scientific advances that expand its capabilities, enhance translational relevance, and position the Center for its next phase of innovation. The proposed developments will ensure continued national access and leadership in simulation- based imaging research and accelerate the development and evaluation of imaging technologies and AI tools.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Fibrotic lung diseases represent a heterogeneous group of diseases resulting from a variety of genetic defects, immune conditions, or inhalational exposures. Fibrosis can manifest in the lung airways in the form of bronchiolitis obliterans (BO) or in the lung parenchyma as in idiopathic pulmonary fibrosis (IPF). Effective pharmacological treatments are lacking for lung fibrosis. As a result, fibrotic lung diseases such as BO and IPF are associated with high morbidity and mortality, and understanding mechanisms of lung fibrosis represents a critical area of need. In this proposal we seek to answer a fundamental question in lung fibrosis – what are the mechanisms that lead to fibroblast activation from normal pulmonary fibroblasts to abnormal fibroblast populations. Supported by novel preliminary data, we identified CTHRC1, a secreted glycoprotein, as important not only as a marker of fibroblast activation, but as a key regulator of fibroblast activation upstream of TGFβ signaling, an important pathway implicated in fibrosis phenotypes. In Aim 1, we will evaluate important airway epithelial cell-fibroblast interactions contributing to CTHRC1- mediated fibroblast activation in the context of bronchiolitis obliterans, a fibrotic airway disease. In Aim 2, we will evaluate alveolar epithelial cell-fibroblast interactions contributing to CTHRC1- mediated fibroblast activation in the context of idiopathic pulmonary fibrosis, a fibrotic lung parenchymal disease. We will leverage Duke University’s status as one of the largest lung transplant centers to directly investigate these questions in human explanted lungs using single cell and spatial transcriptomic analysis as well as sophisticated primary human cell culture techniques. These studies will demonstrate a common mechanism of fibroblast activation in different lung fibrotic diseases and identify a novel therapeutic target in CTHRC1 and its downstream effectors for treatment of fibrotic lung disease. The data gathered during this award period coupled with ongoing mentorship and a multi-disciplinary research environment will prepare me to fulfill my long-term goal of becoming an independent physician-scientist.

NIH Research Projects · FY 2025 · 2025-09

Smoking as little as once every few days significantly increases the risk of all-cause mortality. People who smoke lightly (<10 cigarettes per day (CPD)) and intermittently (PSLI), now represent over 50% of all current smokers. Most interventions for PSLI have applied treatments found to be effective for those who smoke >10 CPD rather than create interventions tailored to the unique challenges faced by PSLI. Existing interventions for those who smoke >10 CPD mostly use pharmacological and behavioral strategies aimed at attenuating symptoms of nicotine withdrawal. These strategies have shown limited success among PSLI, which is not surprising given PSLI typically report low or no nicotine withdrawal. We successfully conducted a small lab-based pilot study to establish feasibility, acceptability, and a signal for efficacy of a cue-based treatment for smoking cessation among PSLI. First, we piloted interactive texting where PSLI sent us pictures of cues from their natural environment. We included these pictures in the lab-based cue-based treatment to help reduce the salience of smoking cues among PSLI. Second, we asked PSLI to text us when they experienced a cue in their natural environment and then texted them a “retrieval cue” that had been linked with not smoking during lab-based cue-based treatment. We paired this cue-based treatment with standard cognitive behavioral treatment (CBT) that included eight weeks of SMS texting support messages and compared our intervention to CBT only. The next step is to adapt this promising intervention into one that is delivered remotely. Thus, an important next step is to adapt the intervention to a telehealth intervention and test it in a larger pilot to firmly establish feasibility, acceptability, and a signal for efficacy. We will work closely will our Community Advisory Board to refine our previously tested intervention and to guide all aspects of the pilot study. The overall aim of this study is to develop a cue-based treatment that can be delivered via telehealth and test whether it outperforms a standard treatment in promoting smoking cessation among PSLI. We propose a two-arm randomized feasibility trial: Arm 1: standard treatment (n=30), and Arm 2: enhanced cue-based treatment (n=30). The primary objective of this trial is to test the feasibility and acceptability of a remotely delivered cue-based treatment intervention to promote cessation in PSLI. Secondary objectives are to assess the preliminary efficacy of the intervention on smoking cessation and to examine the effect of the intervention on cue-reactivity.

NIH Research Projects · FY 2025 · 2025-09

There are approximately three million individuals with perinatally acquired human immunodeficiency virus (pnHIV) worldwide and >150,000 new youth with pnHIV (YpnHIV) annually. The incidence of pnHIV has declined dramatically over the last two decades, yet rates remain high in Eastern and Southern sub-Saharan Africa (SSA) - threefold greater than the global average. Prior studies from SSA have been unable to resolve whether pnHIV increases risk of cardiac or physical dysfunction compared to unexposed children or those with perinatal HIV exposure but who are HIV seronegative (PHEU). Elucidating determinants of cardiac, autonomic and physical dysfunction in the disproportionately affected population of YpnHIV is critical to identify, treat and prevent cardiac comorbidities. Comprehensive phenotyping that combines cardiac imaging, physical function assessment, autonomic dysfunction and molecular profiling represents an innovative approach to fully characterizing the earliest manifestations of subclinical disease thereby enabling early intervention. Thus, the overall objectives of this application are to identify imaging and proteomic determinants of cardiac dysfunction in YpnHIV compared to the HIV unexposed (HU), and to evaluate effects of YpnHIV on physical performance and heart rate variability. Our central hypothesis is that YpnHIV have worse cardiac and physical function compared to HU and PHEU. Our hypothesis builds on our preliminary data which identified a high burden of early cardiac dysfunction on echocardiogram among YpnHIV, and preliminary data linking subclinical cardiac dysfunction among YpnHIV to proteomic profiles indicating cardiac fibrosis, inflammation, and immune activation from a large HIV care program in western Kenya. We will test our central hypothesis through the following Specific Aims: (1) Determine if pnHIV is associated with cardiac dysfunction and abnormal physical performance in children and young adults; (2) Identify mechanisms of subclinical cardiac dysfunction and related biomarkers in YpnHIV using high-throughput proteomic profiling; (3) Measure the effect of HIV status on longitudinal change in cardiac and physical function during a period of rapid adolescent growth. To achieve our Aims, we will conduct a prospective study of a total of >400 YpnHIV, PHEU and HU in western Kenya who are engaged in a long-term, population-based HIV clinical care program – the Academic Model Providing Access to Healthcare (AMPATH) Kenya Program. We will perform echocardiograms, a clinical assessment, six-minute walk testing, heart rate variability assessment and collect biospecimens for molecular profiling through proteomics. In a sub-sample, we will repeat measures at 24 months to determine whether YpnHIV, PHEU and HU status impacts progression of cardiac or physical dysfunction. Our scientific contribution is expected to be significant because we are addressing a dire health comorbidity for YpnHIV with deep clinical, imaging, functional and biomarker phenotyping in an endemic population. As a consequence of the work proposed, we expect to uncover novel insights and a deeper understanding of the pathobiology of contemporary HIV-related cardiovascular comorbidities.

NIH Research Projects · FY 2025 · 2025-09

Asymptomatic carriage of MDROs increases the risk of infection for the carrier and members of the community to whom they may transmit the organism. Due to the increased incidence of community-acquired MDRO infections, there is a pressing need to identify factors influencing MDRO colonization. While diet has been shown to modulate the dynamics and metabolite profiles of human gut microbiota, we lack a detailed and quantitative understanding of how specific dietary factors and human gut microbiome impact MDRO carriage. By integrating a longitudinal human study coupled to a novel sequencing-based approach to elucidate dietary species, advanced computational modeling, high-throughput construction of human gut communities and germ-free mouse experiments, will revolutionize our understanding of the multi-scale fiber-dependent interaction networks shaping MDRO (Clostridioides difficile, vancomycin-resistant Enterococci, third generation cephalosporin- resistant Enterobacteriaceae, and carbapenem-resistant Enterobacterales) fitness and colonization of the mammalian gut. In Aim 1, we will perform a prospective, longitudinal study of community-based participants to elucidate the mappings between diet, human gut microbiome taxa, microbial pathways and MDRO carriage. We will go beyond food intake self-reporting which is limited by systematic biases, to track human diet using a DNA sequencing methodology referred to as FoodSeq. Leveraging the longitudinal data, dynamic computational modeling will reveal microbe-microbe interaction networks across individuals. To identify the key dietary fibers and human gut species shaping MDRO colonization, we will use a high-throughput and automated human gut community culturing pipeline. The species identities as well as the specific combinations of these species will be selected using a novel microbial genome-to-function deep machine learning model (data-driven Community Genotype-Function or dCGF) that predicts MDRO abundance as a function of dietary fibers and the genetic features of constituent community members. Using this expanded design-test-learn (E-DTL) approach, we will identify combinations of species and dietary fibers that significantly influence MDROs fitness in human gut communities. Analysis of the model using explainable artificial intelligence techniques will reveal genes/pathways within constituent community members that impact MDRO fitness, providing mechanistic insights beyond the taxonomic level. In Aim 3, we will use dCGF to design robust and maximally inhibitory or enhancing species- fiber combinations for characterization in a murine C. difficile model. We will evaluate the ability of these designed species-fiber combinations to decolonize C. difficile from the murine gut. Overall, this proposed research will provide critical data and models across multiple scales to understand the interplay between diet, the gut microbiome, and MDRO carriage. Our results will inform future interventions aimed at reducing MDRO carriage, transmission, and infections in the community. Finally, this systems biology framework will be generalizable to study other bacterial pathogens, environmental factors, and microbiome functions.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY This study, in response to RFA-MH-25-195, aims to enhance, deploy, and rigorously validate the Duke Predictive Model of Adolescent Mental Health (Duke-PMA), which uses a novel clinical signature derived from affordable, accessible measures to identify youth at high risk for psychiatric illness in primary care settings. The Duke-PMA, a neural network-based predictive tool, has already demonstrated high accuracy in predicting psychiatric risk one year in advance in youth aged 10-15, using data from the Adolescent Brain and Cognitive Development (ABCD) study. Notably, sleep disturbances have emerged as a key modifiable predictor in the model. Unlike most predictive models that rely on current symptoms to anticipate outcomes, the Duke-PMA bases its predictions on underlying disease mechanisms and protective factors, making it better suited to inform preventive interventions. Furthermore, the model identifies an elevated p-factor, a general measure of psychopathology that spans multiple psychiatric conditions, making it broadly applicable across diverse youth populations. Our project will begin by optimizing the Duke-PMA through the incorporation of behavioral tasks from the NIH Toolbox to enhance its prediction performance. Following Duke AI Health’s Algorithm-Based Clinical Decision Support Oversight framework, we will ensure the model adheres to the highest standards of transparency, quality, and equity. Additionally, we will apply trustworthy AI techniques designed to reduce effects of distribution shifts on model performance to ensure the model remains effective and equitable across diverse clinical settings and demographic groups. After optimization, the Duke-PMA will be deployed in rural primary care and pediatric clinics, where access to mental health services and research participation is often limited. We will enroll 2,000 youth from rural clinics in the Southeast and Midwest, partnering with the Science, Technology, and Research (STAR) Clinical Research Network. We will also explore the benefit of adding a measure of home environments to the Duke-PMA through digital envirotyping, which uses an AI-driven approach to assess home environments remotely without requiring in-person visits, making it much more resource-efficient and accessible than current approaches. Model performance will be validated through psychiatric diagnostics conducted one year after the initial assessment. If successful, this project has the potential to transform mental health resource allocation particularly in underserved communities by offering an accessible, low-cost, data-driven approach to identify vulnerable youth and highlight modifiable risk factors for early intervention.

NIH Research Projects · FY 2025 · 2025-09

Per- and poly-fluoroalkyl substances (PFAS) exposure is widespread and has sparked major concerns about health impacts in highly contaminated communities. The Feng lab has made significant contributions to our understanding of the reproductive toxicity of PFAS by studying PFAS mixtures that replicate PFAS levels in highly contaminated drinking water, as well as emerging PFAS compounds such as perfluorobutane sulfonic acid (PFBS). We have reported that maternal exposure to PFAS mixtures and PFBS at environmentally relevant doses leads to adverse birth outcomes through dysregulation of placental function and fetal neurodevelopment. Recently, our preliminary data demonstrated that exposure to a PFAS mixture or PFBS leads to oxygen accumulation in placentas and embryos during pregnancy and induces mitochondrial oxidative stress in both human placental trophoblast stem cells and murine fetal brains. In this proposal, we will thoroughly examine our hypothesis that PFAS exposure is detrimental to placental function and fetal development via disruption of mitochondrial activities and metabolism. Our specific aims are to: 1) Investigate mitochondrial perturbations associated with PFBS exposures in human placental trophoblast cells; 2) Determine which specific mitochondria- relevant syncytiotrophoblast functions are altered by PFBS exposure; and 3) Assess alterations in placental hemodynamics, oxygenation, and mitochondria-relevant metabolism by PFBS and PFAS mixtures in mice. Novelty: We will address the health impacts of PFAS mixtures that mimic highly contaminated community drinking water and specifically focus on an emerging PFAS compound, whereas most previous studies focused on single legacy compounds and used less clinically applicable exposure levels. In addition, unique, optimized, physiologically relevant human placental trophoblast stem cell-derived organoid models will be used to model the human maternal-fetal interface in vitro. Finally, an innovative, in vivo photoacoustic imaging system will be used to study placental hemodynamics in a mouse model longitudinally. This study will uncover potential druggable intervention targets (mitochondrial oxidative stress) that might mitigate the adverse effects of perinatal PFAS exposure. Furthermore, this study will advance our understanding of gender-specific health effects of perinatal PFAS exposure that will undoubtedly have implications for personalized diagnostics and therapeutic interventions. Feasibility: Combining expertise and preliminary studies provides a strong foundation for this proposal. Dr. Feng’s lab has established the perinatal PFAS exposure in vitro and in vivo models; this proposal is an extension of her previous projects. Dr. Schust's lab has extensive experience working with placental trophoblast-derived organoids and developed the novel properly polarized system used here. Dr. Yao’s lab has focused on developing novel photoacoustic technologies for assessing tissue hemodynamic parameters. Dr. Santos is an environmental toxicologist focusing on mitochondrial toxicants. Our study will significantly contribute to our understanding of the health impacts of PFAS and provide clues for intervention strategies.