University Of Illinois At Chicago

NIH Research Projects · FY 2026 · 2026-02

Peptidyl-tRNA hydrolase (Pth) is an essential bacterial enzyme critical for protein synthesis and whose inactivation leads to cell death. Pth recycles peptidyl-tRNAs that prematurely dissociate from the translated ribosome. Additionally, Pth is involved in rescuing the large ribosomal subunit that remains associated with peptidyl-tRNA after the splitting of the stalled ribosome. It may also contribute to other ribosome rescue mechanisms. Despite its essentiality, efforts to identify Pth inhibitors have been hindered by challenges in generating suitable substrates for high-throughput screening (HTS). To address this, we designed an oligonucleotide-based Pth substrate that mimics peptidyl-tRNA and used it to develop and validate an HTS assay based on the Förster resonance energy transfer (FRET). Limited preliminary studies have identified several compounds with inhibitory activity against Pth from Escherichia coli and Klebsiella pneumoniae. This proposal aims to advance Pth inhibitor development by synthesizing and optimizing derivatives of the most potent hits with sub-micromolar IC50 values to enhance their activity against K. pneumoniae Pth and improve cellular uptake. Concurrently, we will expand the screening campaign to identify novel inhibitor scaffolds. The interaction of these compounds with Pth will be studied by SPR and X-ray crystallography, to guide medicinal chemistry optimization. In addition, we will characterize their mechanisms of action, cellular targets, effects on protein synthesis, and potential resistance mechanisms. The anticipated outcome is the identification of a lead inhibitor scaffold suitable for preclinical development as a foundation for a novel antibiotic platform.

NIH Research Projects · FY 2026 · 2026-02

Project Summary The host lipid environment is a well-described barrier to bacterial infection at several sites including the skin, lung, and upper airways. In addition, both bacterial and host lipids serve as signals that promote inflammation. Bacterial lipids activate Toll-like receptors which initiates cytokine production, immune cell recruitment, and induction of oxidative burst to control infection. Several pathogenic bacteria evade these lipid-mediated defenses by secreting lipolytic enzymes (lipases). Bacterial lipases can cause host membrane dissolution, facilitate escape from cellular compartments, disrupt physical barriers, detoxify antimicrobial lipids, and release nutrient fatty acids. Despite these established contributions of lipases to infection there exist critical gaps in knowledge on how lipases drive host immunity and physiology. This is especially true for Staphylococcus aureus which produces three lipases that have long been presumed to promote virulence, despite scant mechanistic studies. Recent work from our lab started to fill this gap by providing evidence for a direct role for lipases in immune evasion and nutrient acquisition during infection. We made the unexpected observation that the S. aureus secreted lipase, Geh, inhibited activation of innate immune cells in culture. Further, Geh blunted pro-inflammatory cytokine production during infection and was responsible for bacterial persistence in some tissues but not others, highlighting tissue-specific contributions of Geh to survival. The blunted cytokine response was not due to direct functions of Geh on mammalian cells, but rather a result of inactivation of S. aureus lipoproteins, a major pathogen-associated molecular pattern (PAMP) of Gram-positive bacteria, via ester hydrolysis. While our studies indicate S. aureus uses lipases to inactivate lipoprotein PAMPs containing saturated straight and branched chain fatty acids that it synthesizes de novo, it can also acquire host unsaturated fatty acids in a lipase-dependent manner and use them for lipoprotein biogenesis. We found that S. aureus uptake of host fatty acids disrupted innate immune responses and infection dynamics in tissues involved in lipid metabolism via attachment of host fatty acids to bacterial lipoproteins. This deleterious response correlated well with Toll-like receptor 2 signaling. Finally, infections of the skin uncovered a role for Geh that appeared to be independent of Toll-like receptor 2 and instead was linked to liberation of host fatty acid esters that promoted fitness. Altogether, our data support the hypothesis that S. aureus lipases modify both bacterial and host lipids to calibrate innate immunity and promote a fitness advantage that allows adaptation to a wide range of tissues during infection. Aim 1 will determine the cellular and molecular basis for Geh-mediated infection persistence. Aim 2 will test the roles of the three lipases secreted by S. aureus in virulence, lipid utilization, and immune defense. Aim 3 will determine how lipases disrupt immunity and allow S. aureus to adapt to increased circulating lipids in the host.

NIH Research Projects · FY 2025 · 2026-01

As cannabis use patterns rapidly evolve due to changes in legality and societal norms across the United States, it is crucial to identify risk factors of problem use in vulnerable populations. Recent widespread legalization of recreational cannabis has coincided with an increased prevalence of problem use and cannabis use disorder (CUD), with females and those with a psychiatric diagnosis displaying unique risk factors. Females exhibit stronger cannabis withdrawal symptoms and a faster transition from first use to CUD than males, suggesting hormonal risk factors for problem cannabis use. The reproductive-aged window is especially imperative to examine for risks of problem use, given the potential for childbearing and established risks to the offspring of pregnant cannabis users. Cyclical changes in progesterone (P4) and estradiol (E2), such as across the menstrual cycle, could contribute to problematic cannabis use in some individuals. Observational evidence suggests that periods of rising E2 (periovulatory phase), as well as periods of declining P4 and E2 (perimenstrual phase), could be windows of acute risk of problem cannabis use. However, there is a complete lack of experimental research testing a causal relationship between acute menstrual cycle hormone changes and cannabis use. In alignment with the 2022-2026 NIDA Strategic Plan to expand our understanding of the biological mechanisms underlying drug use (Goal 1.1), the proposed project seeks to examine what ovarian hormone mechanisms may give rise to windows of acute risk for problem substance use. Specifically, we plan to use a novel computational modeling approach that leverages generalized additive mixed models and group iterative multiple model estimations to model drug use across the menstrual cycle. These techniques consider both individual and group-level effects. The proposed study will utilize archival data from two of the primary mentor’s recently completed RCTs to evaluate the effects of the menstrual cycle on acute cannabis use risk in a transdiagnostic sample of naturally cycling psychiatric outpatients. Further, the primary mentor’s experimental work indicates that ovarian hormone-driven changes in acute mood and cognitive deficits can be prevented by administering stabilizing doses of E2 and P4 perimenstrually. The proposed study will build upon these findings and be the first of its kind to experimentally examine the perimenstrual drop in E2 and P4 as a potential mechanism of female-specific acute cannabis use risk. Identifying windows of immediate vulnerability to problem drug use will benefit the scientific and clinical communities from both diagnostic and intervention standpoints. Providers will gain additional context to deliver precise, accurate diagnoses and subsequently be able to deliver personalized, temporally-specific treatment interventions. The proposed project and training plan will provide the candidate with comprehensive mentorship from a team of experts, equipping the candidate with the analytic skills and scientific knowledge required to become an independent investigator.

NSF Awards · FY 2026 · 2026-01

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need at the University of Illinois Chicago. A total of 44 scholars pursuing bachelor's degrees in Chemistry and Biochemistry will receive scholarships of up to $15,000 per year for up to five years. Scholars will receive faculty mentoring, and the project will build strong scholar cohorts through student success workshops and courses and a suite of career development opportunities. Professional development activities for the scholars will include mentored research, internships, providing academic support to peers, and participating in faculty-supervised projects to build their scientific writing or communication skills. The overall goal of this Track 2 Scholarships in STEM project is to increase STEM degree completion of academically talented, low-income undergraduates with demonstrated financial need. There is a significant national need to grow the STEM workforce and nurture key talent that will ensure economic competitiveness and provide domestic leadership across critical sectors. This project directly speaks to this need by supporting STEM student success, which will strengthen the workforce in chemistry, biochemistry, biotechnology, and other key areas of need. As future professionals, many project scholars will play an active role in the translation of innovations in the chemical sciences to economically viable, market-driven products and services. The project will be assessed by an experienced evaluator, and the data generated will contribute to the knowledge base regarding effective strategies to support talented, low-income students in STEM. This project is funded by NSF's Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of academically talented, low-income students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students. 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 2026 · 2026-01

With support from the Improving Undergraduate STEM Education: Hispanic-Serving Institutions (HSI Program), this Level 2 Implementation and Evaluation project at the University of Illinois Chicago aims to strengthen student learning and success in core programming courses. The project will revamp four core programming courses required for all majors to better align content, student supports, and teaching practices across sections. A collaborative teaching model will allow for more centralized creation of course content and increase faculty contact time and student engagement. The project will also develop an Igniting Minds program that will focus on boosting academic outcomes for students with less programming experience than their peers prior to enrolling in the university. Strengthening student outcomes in computer science courses and programs is an important step to meet the needs of the modern STEM workforce. The goals of this project, which align with this critical national need, are as follows: (1) develop a program to engage the growing pool of computing talent; (2) drive positive change in the programming core sequence for both instructors and students; and (3) improve retention and success for all students. Project research will investigate the impact of the project on student satisfaction and success and will also explore impacts on faculty and student engagement, collaboration, and other factors. Data streams include surveys and focus groups. The project will be evaluated by an independent evaluator to assess progress towards stated goals. This project is funded by the HSI Program, which aims to enhance undergraduate STEM education, broaden participation in STEM, and increase capacity to engage in the development and implementation of innovations to improve STEM teaching and learning at HSIs. 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 · 2025-10

With artificial intelligence (AI) increasingly embedded into critical software systems—from healthcare to defense applications—ensuring these AI-enabled software solutions (a.k.a. AI-Software) meet comprehensive responsibility standards has emerged as a pivotal challenge. Traditional software debugging methods, centered around identifying and rectifying defects, fall short in addressing the unique nature of AI-software. This project seeks to develop novel, principled debugging techniques specifically designed to detect and resolve responsibility - that is, issues related to transparency, accountability, and impartiality - before AI-software is deployed publicly. The project also includes education-related activities, where results from the research will contribute to the computing curriculum expansion. The project proposes the development and evaluation of a transformative debugging framework tailored to AI-software, which incorporates pre-trained deep neural networks and large language models during execution. At the core of this framework is the novel concept of metamorphic debugging, which explores the intersection of metamorphic testing - a method analyzing output variations caused by structured input changes - and relational verification across three dimensions: causality, information theory, and extreme value theory. Unlike conventional debugging, which inspects single program executions independently, metamorphic debugging simultaneously examines relationships across multiple program-runs to uncover nuanced requirement violations. The research will include creating methods for generating intuitive test cases sensitive to causal relationships, developing techniques for elucidating higher-order relations among multiple execution traces, and applying statistical approaches to rigorously assess worst-case scenarios for undetected defects. The framework's effectiveness will be routinely validated through integration into real-world AI-software, collaborations with industry partners, and surveys assessing student and developer perceptions of responsible AI-software. 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 · 2025-10

As high-performance computing (HPC) systems grow increasingly heterogeneous and application workloads become more diverse, achieving both high performance and energy efficiency poses a significant challenge. While performance optimization remains essential, energy efficiency has emerged as a critical priority due to substantial infrastructure demands, operational costs, and environmental impact. This project tackles two fundamental barriers to energy-conscious HPC: power waste in heterogeneous hardware and the lack of dynamic power coordination. It develops a holistic framework, EcoHPC, to enable energy-efficient execution of hybrid workloads on heterogeneous systems. The anticipated outcomes have broad scientific, economic, and environmental impacts. Additionally, an integrated education plan aims to train the next generation of the HPC workforce. The project introduces three core technical innovations. First, it exploits collaborative filtering-based recommendation systems that combine offline analysis with real-time profiling to model application performance-power trade-offs and guide scheduling decisions. Second, it applies multi-objective optimization and Pareto-front analysis to treat power as a first-class schedulable resource, enabling system-wide coordination and optimization. Third, it develops adaptive runtime systems that dynamically predict application phases and resource demands, allowing applications to minimize power waste while maximizing performance under power constraints. Together, these innovations yield new workload models, energy-aware allocation methods, and runtime strategies that significantly enhance energy efficiency in heterogeneous computing environments. 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

Diabetes is a complex disease that disproportionately affects Black and Hispanic/Latino individuals in historically underserved communities. Treatment often requires interdisciplinary collaboration between multiple professions in healthcare and community-based settings. Social determinants in underserved communities create additional barriers to receiving care. This creates an uneven distribution of diabetes risks, incidence, and control. Challenges to receiving care have been categorized as three C’s: concern (e.g., medical mistrust), convenience (e.g., barriers to access), and complacency (e.g., limited capacity to make lifestyle changes). Federally qualified health centers (FQHCs) play a key role in brokering linkages between healthcare specialists and community-based trusted messengers to address these challenges. FQHCs serve patient populations in communities with high rates of diabetes and work to address barriers to care through beneficial community-clinical linkages. FQHCs are located in community settings and recruit staff that reflect the diversity of their communities. In this study, we plan to evaluate community-based diabetes programs in two FQHCs that serve similar populations, but in different regions of northern Illinois. Both organizations have programs that embed specialists into community settings. The first brings FQHC has an endocrinologist onsite once per week. The second engages pharmacy teams in community-based care. The goal of this study is to evaluate the impact of these FQHC programs, including best practices for engaging Black and Hispanic/Latino patients. Our mixed-method design is grounded in CBPR, which values people’s lived experiences and integrates their expertise into the understanding of community-based phenomena. As part of this study, we will first leverage clinical data and a quasi-experimental setting to measure the impact of these programs on patients’ diabetes control (e.g., A1c values) and care plan adherence. We will use difference-in-differences models to see whether engaging in an FQHC-sponsored diabetes program in a community setting is associated with better A1c control than similar care in a clinical setting. Building on the quantitative findings, we will host a series of interviews with patients to further understand how patients navigate complex diabetes care and the barriers they experience when doing so. In addition, we will plan to distribute a scenario-based survey to develop a scale for assessing potential barriers to diabetes care and the tactics for overcoming those barriers. This survey will ask participants to make tradeoffs between different factors to understand the aspects of different programs that make them more or less successful. Finally, we will conduct focus groups with key stakeholders and community leaders to understand the sustainability of these programs. The successful completion of this study will enhance our understanding of the impact of diabetes treatment location on patient outcomes and provide insight into what aspects of the program contribute to program effectiveness. We will learn how FQHCs create linkages to diabetes care for community members.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY This proposal outlines a five-year plan to prepare Dr. Gege Yan for an independent research career. The plan aims to enhance Dr. Yan's expertise in cardio-oncology through specialized training in sequencing technology, stem cell biology, mitochondrial biology, and oncology. During the K99 phase, Dr. Yan will be guided by the advisory committee, take formal courses, complete demanding research projects, and join a career transition program. Cancer ranks second in global mortality, preceded only by cardiovascular diseases. Targeted therapies have improved cancer patient outcomes, but shared signaling pathways between cancerous and noncancerous cells can result in cardiac toxicity from anticancer drugs instead of the cancer itself. Ponatinib, a tyrosine kinase inhibitor (TKI) used to treat chronic myeloid leukemia (CML), was temporarily withdrawn from the market a decade ago due to severe cardiac and vascular toxicity. It serves as a last resort for CML patients when other TKIs fail to halt cancer progression. Ponatinib inhibits over 60 kinases, making understanding its cardiac toxicity challenging. There has been a lack of emphasis on enhancing its anticancer efficacy while protecting the heart. Dr. Yan utilized human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) to model ponatinib- induced cardiomyocyte injury and found that it's linked to the activation of the integrated stress response (ISR) pathway through proteomic analysis. She identified general control nonderepressible 2 (GCN2) as the eIF2α kinase responsible for relaying mitochondrial stress signals to trigger the primary ISR effector activating transcription factor 4 (ATF4) upon ponatinib exposure. Mechanistically, ponatinib perturbed mitochondrial function results in ATP deficits and subsequently triggers GCN2-mediated ISR activation. Moreover, administering the ISR inhibitor ISRIB is cardioprotective against ponatinib when given at disease onset both in vitro and in vivo. Importantly, ISRIB does not affect the antitumor effects of ponatinib in vitro. In this study, to determine whether GCN2-mediated ISR regulates ponatinib-induced cardiotoxicity is specific to cardiomyocytes, Dr. Yan will use hiPSC-derived endothelial cells, human primary cardiac fibroblasts, and a cardiac-specific knockout mouse model to explore the role of GCN2 in ponatinib-induced cardiotoxicity in vitro and in vivo (Aim 1). She will employ ATF4 ChIP-seq and phospho-profiling to identify the precise downstream signature of GCN2 that drives ponatinib-induced cardiotoxicity (Aim 2). Furthermore, she will investigate whether administering ISRIB after the manifestation of ponatinib-induced cardiotoxicity, which is more clinically relevant, is protective. Moreover, she plans to use a xenograft mouse model to investigate whether ISRIB can enhance the antitumor efficacy of ponatinib (Aim 3). In summary, this study proposes an innovative solution to reduce ponatinib-induced cardiotoxicity, offering the potential for improved and extended lives for cancer survivors. Furthermore, completing these studies will lay the foundation for Dr. Yan's transition to her own independent research program.

NIH Research Projects · FY 2025 · 2025-09

Project Summary/Abstract Asthma is a prevalent chronic disease with no current cure, despite recent improvements in diagnosis and management. The "hygiene hypothesis" suggests that early exposure to microbes protects against asthma, which has gained support from increasing evidence, but the underlying mechanisms remain unclear. Our long- term goal is to uncover the molecular mechanisms responsible for the protective effects of early endotoxin exposure against asthma. The objective of this proposal is to examine the role for Miz1 in asthma with relation to the “hygiene hypothesis”. Our central hypothesis is that endotoxin-induced downregulation of Miz1 provides protection against allergic asthma through epigenetic regulation. Our rationale is that identification of the mechanisms to target the Miz1 pathway may offer new therapeutic approaches to prevent and treat asthma. The central hypothesis will be tested by pursuing three specific aims: Specific Aim 1 will investigate the molecular mechanisms of Miz1 downregulation by endotoxin and its role in mediating the protective effect of endotoxin against allergic asthma; Specific Aim 2 will determine the underlying molecular mechanisms by which endotoxin-induced Miz1 downregulation confers protection against allergic asthma; Specific Aim 3 evaluate the therapeutic efficacy of Miz1 pathway targeting in an experimental asthma model and determine whether components of the Miz1 pathway are deregulated in allergen-challenged human asthmatic. This study is significant, as identification of new targets of the Miz1 pathway may lead to the development of novel therapeutics for asthma, a debilitating condition with no targeted or efficient treatment. The proposed research is conceptually innovative because Miz1-mediated epigenetic regulation may provide a long-sought “missing link” for the association between earlier endotoxin exposure and decreased asthma prevalence. We also utilize innovative approaches, which combine cutting-edge analytical and manipulative methods in both cellular, animal, and human studies. These investigations are impactful as they may provide potential mechanisms for the “hygiene hypothesis” and fundamental insights into the pathogenesis of asthma, leading to new therapeutic targets.

NIH Research Projects · FY 2025 · 2025-09

PROJECT ABSTRACT Sarcoidosis is a systemic disease of unknown etiology that results in granulomatous inflammation which, if persistent, can lead to fibrosis of involved organs and subsequent dysfunction. Advanced pulmonary sarcoidosis-related fibrosis (APSF) occurs in up to 20% of those with pulmonary sarcoidosis and carries significant morbidity and mortality. Existing transcriptomic studies within sarcoidosis highlight multiple peripherally circulating dysfunctional cell subtypes and aberrant pathways that drive sarcoidosis inflammation; however, knowledge of these mechanisms in the APSF phenotype is limited. Improved understanding of the mechanisms by which dysregulated immunity contributes to APSF is critical to identify future therapeutic strategies that benefit this vulnerable population. Our preliminary data suggest that subsets of circulating CD14+ monocytes exhibit unique profibrotic programming in APSF compared to non-fibrotic disease, with further profibrotic signaling originating from circulating central memory T cells and deserves further exploration. By integrating current knowledge of sarcoidosis pathophysiology, emerging concepts in idiopathic pulmonary fibrosis (IPF), and our compelling preliminary data, we aim to elucidate the mechanisms through which circulating CD14+ monocytes drive the inflammatory-fibrotic axis of APSF. In Aim 1, we will use single- cell RNA sequencing (scRNA-seq) and surface protein indexing to identify key regulators and corresponding signaling pathways within subpopulations of CD14+ monocytes in individuals with chronic sarcoidosis, with and without APSF. Key transcription factors (TFs) identified will be tested in an in vitro functional assay of monocyte-to-fibrocyte differentiation to confirm their profibrotic effects. Aim 2 will define the external profibrotic influences monocytes receive from TCM cells leveraging pathways uncovered by scRNA-seq through in vitro methods of cell stimulation, co-culture, and differentiation assays. We anticipate that our findings will identify novel mechanistic pathways involved in the progression of APSF that may be used for therapeutic targeting. In addition, a complimentary career development plan has been proposed to achieve my goals of becoming an independent investigator and future expert in pulmonary fibrosis immunology. Over the award period, I will refine analytical techniques of large, multifaceted datasets and strengthen my expertise in translating gained insights into biologically relevant in vitro models. Through this process, I will also advance my skills in study design, statistical methods, and result dissemination, building a strong foundation for future R01 funding as a independent physician scientist. 1 | Uncovering the Inflammatory-Fibrotic Axis in Pulmonary Sarcoidosis Christen Vagts, MD Project Abstract and Narrative

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Next-generation hearing assistive technology will be driven by digital processing and digital connectivity, but both can introduce disturbing delays. Advanced signal processing algorithms like noise reduction and dereverberation perform best when they are allowed longer processing delay, and digital wireless devices like remote microphones can greatly improve intelligibility but introduce significant transmission delays. Today, hearing aids use strict delay constraints of about ten milliseconds in all conditions. However, listeners might be able to tolerate greater delay in some conditions, especially in exchange for higher-quality signals. A better understanding of tolerable limits for processing delay and transmission delay would help engineers to develop innovative digital algorithms and system architectures for hearing technology. This project will measure delay tolerance under realistic conditions using a set of experiments in which two subjects hold a spontaneous conversation using hearing devices with variable delays. First, the researchers will validate this new protocol and compare it against conventional listening and speaker tasks. Next, the researchers will measure tolerable transmission delays for wireless remote microphones, which have been shown to dramatically improve intelligibility in noise but are limited by the high latency of digital wireless protocols like Bluetooth. Finally, they will study the effects of environmental noise and reverberation on delay tolerance. If users can tolerate greater delay in challenging noisy and reverberant environments, then algorithms could dynamically adjust their delay and enhancement performance based on acoustic conditions. Delay is arguably the greatest technical roadblock to developing innovative connected hearing systems that could dramatically improve quality of life for people with hearing loss. Before technology developers can hope to address the technical challenges, they must better understand the perceptual constraints and tradeoffs.

NIH Research Projects · FY 2025 · 2025-09

Project Abstract Chronic kidney disease (CKD) is present in over 50% of adults with sickle cell disease (SCD) and is associated with increased morbidity and early mortality. However, the mechanisms underlying the development of SCD- related CKD are poorly understood and therapies to prevent and treat SCD-related CKD are urgently needed. Our group has reported that dysregulated tubuloglomerular feedback and proximal tubular stress may be implicated in the pathophysiology of SCD-related CKD. This proposal will leverage our robust preliminary data to target these mechanistic pathways using sodium-glucose cotransporter-2 inhibitors (SGLT2i), a class of drugs representing one of the largest advances in CKD treatment for the general population, although its impact on CKD in people with SCD has not yet been evaluated. SGLT2i have kidney-protective mechanisms that overlap with pathways implicated in SCD-related CKD, including reactivating tubuloglomerular feedback and ameliorating proximal tubular stress, making this class of therapy an ideal candidate for developing a targeted approach to treat SCD-related CKD. Our group has also demonstrated that kidney functional magnetic resonance imaging (fMRI)-derived perfusion, oxygenation, and fibrosis are able to differentiate patients with CKD from healthy volunteers. In the proposed study, we will leverage this noninvasive tool to gain valuable in vivo insights on the impact of SGLT2i in patients with SCD-related CKD. We will test our hypothesis that SGLT2i ameliorates critical pathophysiologic processes in SCD-related CKD via two specific aims. Specific Aim #1 will utilize a transgenic SCD mouse model to evaluate whether the SGLT2i, empagliflozin, improves biomarkers of kidney function and injury (subaim 1) as well as fMRI, histopathology, and gene expression patterns (subaim 2). Specific Aim #2 will investigate whether empagliflozin improves candidate biomarkers of tubuloglomerular feedback and proximal tubular stress (subaim 1) and kidney fMRI-derived perfusion, oxygenation, and fibrosis measures (subaim 2) in a pilot study of SCD patients with CKD treated for 48 weeks. Integrating biomarkers for candidate mechanistic pathways with fMRI will lead to a deeper understanding in the pathophysiology of kidney damage and guide therapeutic strategies for SCD-related CKD. This research team is exceptionally positioned to achieve the goals outlined in this proposal. In addition to our strong history of productivity in SCD-related CKD research and fMRI-related research, we have a robust institutional environment at the University of Illinois Chicago Comprehensive Sickle Cell Center that cares for over 800 SCD patients and has a long-standing tradition of successful implementation of research studies. Developing a better understanding of the pathways and effects of SGLT2i for treating SCD-related CKD has the potential to have a significant impact on this underserved, high-risk population.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Treatment intensification (Trt-I), the adjustment of antidiabetic medication (ADM) usage in response to inadequate glycemic control, is an essential component to effectively manage type 2 diabetes (T2D). However, the selection of ADM and the timing of Trt-I exhibit significant divergences that cannot be solely explained by clinical motivations. Numerous studies have indicated that the diverse Trt-I patterns can be significantly attributed to socioeconomic status (SES). Nevertheless, there remains a gap in understanding whether differences in Trt-I across various SES backgrounds persist over the long-term trajectory of diabetes care, which ultimately modifies the risk of costly and often irreversible complications. To address this knowledge gap, the investigators propose a study aiming to elucidate the pathways from SES to glycemic control and the complications through Trt-I. The research plan includes: (1) providing a comprehensive summary of diverse Trt-I patterns and ADM selection across SES indicators using multi-site electronic health records (EHR); (2) assessing the marginal and intermediary impact of Trt-I on the association between SES and diabetes outcomes using a structural equation modeling approach; and (3) testing the predictive capabilities of SES, Trt- I patterns, and glucose outcomes in relation to the early onset of diabetes complications using machine learning methods. The outlined research will provide insight into the cascading effects from SES to glycemic controls mediated by Trt-I modality, and its impact on diabetic complications. This career development plan is closely aligned with the aims of the research. Both research and training will be mentored by internationally-renowned and well-versed experts in the field of health outcomes research, clinical sciences in endocrinology, causal-inference modeling, machine learning and informatics: Dr. Todd Lee, Head of Pharmacy Systems, Outcomes and Policy of the UIC; Dr. Brian Layden, Chief of the UI Health Endocrinology Division and a steering committee member of the Chicago Diabetes & Training Center; Dr. Ali Cinar, Endowed Chair Professor of the Illinois Institute of Health Technology and Director of an Engineering Center for Diabetes Research and Education; Dr. Gegory Calip, Global Epidemiology Lead of Abbvie Inc.; and Dr. Jacob Krive, lead bioinformatician at UIHealth and Northshore University Health System. Combined with formal didactics, they will provide the support needed to achieve the training aims, developing skills and knowledge in the following areas: (1) Healthcare data analytics using structured equation models within the context of causal inference; (2) Contemporary use of ADM and clinical consensus on treatment decision; (3) Computational science and machine learning techniques specifically applicable to healthcare data analysis; and (4) leadership and grantsmanship skills necessary for directing future research programs. Upon completion of the training aims, I will be uniquely positioned as a lead investigator, having acquired rigorous experiential knowledge in various research methods for real-world health data science.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT The field of ophthalmology has been evolving due to changes and advancements in technology, data science, clinical informatics, and artificial intelligence (AI). Integration of new technologies into clinical practice, research, and educational programs presents both opportunities and challenges. These innovations have the potential to improve patient care, enhance diagnostic accuracy, and drive research in the vision sciences. However, an evolution in clinical training, education, and research practices is necessary to implement new technologies more effectively. Medical education has recognized the need to adapt to new technology, data science, and AI, and close the gap in understanding their use in clinical practice. There are few resources and programs focused on the integration of technology, data science, clinical informatics, and AI in medical education and training in ophthalmology. This meeting will allow for an opportunity to bring together physicians, researchers, and educators within these various disciplines for the shared goal of advancing ophthalmology training, education, and research by integrating new technologies, data science, informatics, and artificial intelligence. Specific aims include: (1) to explore the current state of ophthalmic education and education in vision science, (2) to address the potential gap between the development of new technologies and their implementation into ophthalmology education and training, and (3) to develop recommendations and strategies for leveraging technologies in education, research, and clinical practice effectively and sustainably. This conference will consist of focused sessions through which participants will hear from leaders in their respective fields, and workshops and networking events to discuss and learn methods to implement new technologies into ophthalmology training and medical education. Conference proceedings will be published in specialty journals, ensuring broad accessibility. We plan to submit to both clinical and research journals (e.g. Ophthalmology, Translational Vision Science & Technology, Journal of Academic Ophthalmology). An educator toolkit will be created and distributed, consisting of resources, conference materials, and information designed for program participants. Based on the results of the conference, a list of priorities will be established for future research and work in technology, data science, clinical informatics, and AI in ophthalmology training and education.

NIH Research Projects · FY 2025 · 2025-09

Hypertension and obesity are both major risk factors for cardiovascular disease (CVD), a leading cause of death in the United States (US). Obesity and hypertension can be prevented or controlled with diet, as evidenced in the DASH and ENCORE trials. However, a lack of community access to affordable, healthy foods contributes to poor diet quality. Federally Qualified Health Centers (FQHCs) play a significant role in healthcare delivery for those with hypertension and obesity, and are the bedrock of primary care in medically underserved communities where low access to healthy foods and high levels of chronic disease coexist. FQHCs can enhance access to healthy foods through strong, sustainable relationships between clinics and community-based organizations (CBOs) that are focused on tackling local food needs. The FIM+DASH intervention seeks to leverage FQHCs and CBOs to provide cooking classes, nutrition and HTN education, and home food delivery for adults with obesity and hypertension.1 This research will examine the effectiveness of a 12-week Food is Medicine intervention for individuals who have hypertension and obesity to promote healthy eating and blood pressure control, followed by a 12-week maintenance period. We will conduct the intervention in four urban communities near the University of Illinois FQHCs with well-documented obstacles to accessing healthy foods. We will1) Quantify the effect of the FIM+DASH intervention vs. usual care on blood pressure among individuals attending one of our FQHCs; 2) Evaluate the effect of FIM+DASH vs. usual care intervention on DASH diet adherence(diet quality), body weight and waist circumference, and 3) Identify factors associated with sustainability and scalability guided by the RE-AIM framework (Reach, Effectiveness, Adoption, Implementation, and Maintenance). If effective, FIM+DASH could serve as a model for a sustainable intervention that can be adopted by FQHCs to reduce HTN-related morbidity and mortality among high-risk communities in the US.

NIH Research Projects · FY 2025 · 2025-09

Modified Project Summary/Abstract Section The ENGAGED Medical Rehabilitation Research Center (MRRC) is a bi-institutional collaboration between the University of Illinois Chicago and the University of Texas Health Houston/TIRR, developed in close collaboration with the national disability community and medical rehabilitation research (MRR) organizations. Through cross-partnerships, research teams, and project cores, including shared resources and infrastructure, as well as cross-disciplinary project staff, we will advance the national MRR infrastructure to support rigorous community-engaged research (CEnR) in ways that would not be possible with any single institution or investigator acting alone. The advancement of the science of CEnR for and with the disability community is supported by the three synergistic cores and a multi-phase community-based participatory research project. With leadership from an exceptional multidisciplinary team and guidance from our internal and community advisory boards, we will accomplish the following overall specific aims: Aim 1: Establish a national infrastructure to support meaningful community engagement in MRR to support the co-creation and dissemination of research to support the development of evidence-informed interventions to address social determinants of health. Aim 2: Network with new and established researchers through the provision of technical assistance and consultative support on best practices for community-engaged rehabilitation research. Aim 3: Generate new knowledge on community engagement through the conduct of original research grounded in the appreciative inquiry process. Aim 4: Advance multidisciplinary observational, experimental, and implementation research that intentionally involves people with long-term disabilities, promoting improved health and participation outcomes through pilot funding to support CEnR and partnership building. Aim 5: Generate and curate state-of-the-science knowledge products on community engagement for disability and MRR communities that will be shared through our newsletter, social media, and on our website. Aim 6: Empower disability communities to engage in medical rehabilitation research through the provision of training programs and peer mentorship. Aim 7: Disseminate best practices through a quarterly seminar series and National Symposium on Community Engaged Scholarship for the disability and MRR community. People with lived experience of long-term disabilities are integrated into all elements and components of the ENGAGED MRRC as Research and Core Leaders, as paid research personnel and consultants practicing in their areas of professional expertise, while simultaneously leveraging insights from their lived experiences to enhance relevance and innovation. Innovation: The ENGAGED MRRC will utilize state-of-the-science CEnR approaches, integrated knowledge translation, and extreme citizen science to advance the science of CEnR and generate and disseminate new knowledge about the impact of social determinants of health on individuals with lived experiences of disabilities, thereby identifying intervention targets for medical rehabilitation researchers.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT Infertility affects over 50 million couples worldwide and it is associated with both, obesity and dyslipidemia. Lipids account for ~50% of all cell membranes, ~40% of cells, and play a key role in ovarian function. Despite its abundance, our understanding of the ovarian lipidome, including abundance, distribution, and function within specific ovarian regions, remains at its infancy. This hampers our ability to determine how ovarian dyslipidemia can result in infertility. Advancement in this field has been severely curtailed by: 1) loss of lipids when using common tissue preservation methods, 2) difficulty in lipid annotation, 3) lack of available human ovarian lipidome databases, and 4) difficulty to study lipids while retaining a spatial context. Using state-of-the- art Matrix-Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry Imaging (MALDI-TOF MSI) we are now capable of retaining the spatiality required to study the ovarian lipidome. This approach greatly enhances our ability to identify disruptions in the ovarian lipidome induced by chemical exposures, which is the goal of this proposal. Chemical exposures can induce dyslipidemia in adipose and non-adipose tissues, such as the ovary. One of such chemicals are organophosphate flame retardants (OPFRs), emerging chemicals that are increasingly prevalent human urine and plasma. We and others have demonstrated that OPFRs exposure leads to ovary and ovarian cells’ dyslipidemia, ovarian dysfunction, and poor reproductive outcomes. We hypothesize that OPFR-induced ovarian dyslipidemia is region- and lipid-specific. To address this, we will first develop a spatial ovarian lipidome map of the human and mouse ovary coupling untargeted lipidomics with mass spectrometry and MALDI-TOF MSI. We have demonstrated how this approach can robustly evaluate lipid distribution and abundance within ovarian regions (germ cells, follicles, cortex, and stroma) with superb specificity in both human and mouse ovaries. Capitalizing on this platform, we will next evaluate the impact of environmentally relevant OPFRs exposures on ovarian lipids at environmentally relevant exposures in a mouse model. Altogether, this will provide the first ovarian lipidome map of chemically-sensitive cellular ovarian regions for OPFRs. These results will help infer sensitive ovarian targets of OPFRs disruption. We expect that the results from these studies will provide a platform for discovering new predictors of ovarian dysfunction.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Acinetobacter baumannii is a critical public health threat due to its rapid spread in healthcare facilities and high rates of multidrug resistance. Asymptomatic colonization of the human gut is thought to be a major reservoir for A. baumannii in hospitals. Colonization by Acinetobacter spp. is associated with diet, antibiotics, illness, and hospitalization, all of which can be associated with gut microbiota dysbiosis. Asymptomatic colonization by A. baumannii is associated with increased risk of clinical infections, emphasizing the importance of colonization in A. baumannii pathogenesis. Therefore, it is critical to understand how A. baumannii colonizes the gut to develop new approaches to prevent A. baumannii spread. A major limitation to this approach is the lack of understanding of mechanisms A. baumannii uses to colonize and persist in the host gastrointestinal tract. Therefore, we developed a post-antibiotics mouse model to investigate strategies A. baumannii has evolved to colonize the gut. We discovered that ornithine catabolism is crucial for A. baumannii persistence in the gut and that ornithine catabolism is conserved in most A. baumannii strains. We further determine that ornithine is a non-preferred carbon source that A. baumannii utilizes when competitively excluded from preferred carbon sources by the resident microbiota. We identify transcriptional and post-transcriptional regulators that control genes required for ornithine catabolism. Dietary supplementation of ornithine or a preferred carbon source further promote A. baumannii gut colonization, demonstrating the potential for diet to affect A. baumannii colonization. Our central hypothesis is that A. baumannii senses nutrients in the gut environment to regulate carbon metabolism and compete with the resident gut microbiota by catabolizing ornithine. We will test this hypothesis by determining how A. baumannii integrates nutrient sensing to regulate ornithine catabolism and mechanisms of A. baumannii carbon source preference. We will further define how the microbiota and diet determine A. baumannii ornithine utilization in the gut using genetics, biochemistry, and mouse models. Finally, we will assess the role of ornithine catabolism in colonization and dissemination in clinical isolates from human gastrointestinal tract samples. These studies will identify how A. baumannii regulates carbon source utilization using ornithine catabolism as a model system. The results of these studies will lay the foundation to uncover the fundamental biology of metabolic strategies A. baumannii uses to compete for nutrients in the face of intermicrobial competition. Long-term, these findings will identify potential targets to eradicate A. baumannii from the gut reservoir and prevent healthcare outbreaks.

NSF Awards · FY 2025 · 2025-09

The brain is composed of specific cells called neurons that connect to form circuits. These brain circuits allow the brain to process information, control bodily functions, and regulate behavior. However, these circuits can malfunction, which can lead to brain disorders. This research project aims to address challenges in damaged neuron circuits by "reprogramming" the brain. The long-term goal is to develop new ways to change and repair brain circuits, which will help understand how to treat brain disorders. The project will use zebrafish larvae as a model system to demonstrate that signals can be transferred from a healthy brain to a damaged one with the goal of rebuilding the malfunctioning circuits. The project will engage students and the public in science, technology, engineering, and math (STEM) activities. Local high school students will help build novel brain interfaces to demonstrate control over zebrafish movement. College students will create interfaces to demonstrate signal transfer between zebrafish and robots. These activities will be shared through educational videos and outreach events that will allow the community to learn more about neurons and brain. This research will improve our understanding of brain development and highlight the importance of early intervention for brain disorders. Within the brain, neurons interconnect to form neural circuits, which are essential for processing information, controlling bodily functions, and regulating behavior. However, in disordered brains, neural circuits can malfunction or become malformed, leading to a range of neurological and psychiatric disorders. This project aims to address these maladaptive neural circuits by reprogramming the brain that will ultimately pave the way to reconstruct maladaptive neural circuits by replicating those from a healthy brain. The project will develop a new method to manipulate neural circuit development by transferring and replicating neural signals from one brain to another using an optogenetic actuator and a neural activity indicator in zebrafish larvae and demonstrate a functionally and structurally replicated brain. By demonstrating the feasibility of replicating neural signals and mitigating malfunctioning circuits in zebrafish larvae, this research will lay the groundwork for future research in the field of neural circuit restoration. The overall educational goal is to engage and motivate students and the public in STEM through comprehensive and interactive activities. Toward this goal, the proposed research will be integrated into educational projects to engage student interns from local high schools and undergraduate students at the University of Illinois at Chicago in the development of zebrafish interfaces. In addition, the research will contribute to public education by creating instructional videos for an at-home science project, which will be disseminated via online platforms. 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

There are populations at risk for late stage breast cancer (BC) diagnoses and worse post-diagnosis quality of life, including Latinas, in part due to non-adherence to guideline-concordant screening. Education+navigation (educate) approaches offer community education to address psychosocial barriers (e.g., fear) and navigate individuals to free/low-cost breast cancer care. Our transdisciplinary team has developed a promising empowerment+navigation (empower) approach that may lead to greater BC screening. In the empower approach, individuals who are non-adherent to US Preventive Services Task Force (USPSTF) guidelines learn about BC; are navigated to free/low cost breast cancer care; and gain communication skills to promote BC screening throughout their networks. Our premise is that empowering non-adherent individuals as breast health agents may lead to greater BC screening among non-adherent individuals and their networks than treating non-adherent individuals as passive recipients of education. The current proposal tests our premise and identifies “active ingredients” of the empower approach. In Aim 1, we will conduct an individual randomized controlled trial (RCT). Among non-adherent individuals, we will compare the effects of empower and educate approaches on initial and repeat BC screening, in line with USPSTF guidelines. In Aim 2, we conduct an observational social network study. We will recruit network members through nonadherent individuals enrolled in our RCT. Among network members, we will compare the effects of empower and educate approaches on initial and repeat BC screening across four years. In Aim 3, we will explore theoretical mechanisms of change that could contribute to intervention differences in BC screening. For non-adherent individuals’ BC screening, we will examine the mediating roles of greater BC knowledge and motivation to be healthy role model. For network members’ BC screening, we will examine the mediating role of non-adherent individuals’ enhanced capacity to promote BC screening. Specifically, we will test the independent effects of volunteerism in community BC initiatives, potential to “bridge” network members with formal change agents (e.g., community health workers, navigators), acceptability to promote BC, feasibility to promote BC, and BC promotion to network members. Our innovative, robust approach has direct implications for expediting the translation of promising community interventions into practice.

NSF Awards · FY 2025 · 2025-09

Mathematicians study many different mathematical objects, and some of these objects are more complicated than others. Combining ideas from different areas of mathematical logic, from computability theory, descriptive set theory, and infinitary model theory, the Scott analysis is a way of measuring and understanding the complexity of mathematical objects. The Scott analysis is robust in the sense that it captures several different kinds of complexity that all coincide: the complexity of describing an object, the complexity of identifying two copies of an object, and the complexity of an object's internal structure. Though there remain many questions, the Scott analysis has now been well-developed for the case of discrete structures such as many structures appearing in algebra. More recently there has been increasing interest in studying continuous structures such as those appearing in analysis, a setting in which we do not have a robust Scott analysis, as there are further complication which do not arise in the discrete setting. This project will develop a robust Scott analysis in this continuous setting, including applications, while also further applying developing the Scott analysis in the more classical discrete setting. The long-term goals are to give a more rigorous and formal understanding of why certain mathematical questions are difficult or even impossible to solve, and what the barriers are to solving them. This project includes the training of undergraduate and graduate students. Consider a discrete structure such as a graph, group, or Boolean algebra. The Scott analysis assigns to this structure an ordinal-valued Scott rank or more finely a Scott complexity. This is robust in the sense that it measures, simultaneously, the complexity of defining the automorphism orbits of the structure, the complexity of characterizing the structure up to isomorphism in infinitary logic, the Borel complexity of the set of copies of that structure in the topological space of all structures, and the computational complexity of building isomorphisms between copies of that structure. This project will develop such a theory in the continuous setting where the structures are metric or topological structures such as Banach spaces or manifolds. The main goal is to develop a bridge between syntactic characterizations in infinitary continuous logic and semantic characterizations in terms of computation or topology and descriptive set theory. Applications of this include studying how the Scott complexity of n-manifolds changes as n increases, or of studying the Scott complexity of Banach spaces and, for example, the impact of whether or not they have a Schauder basis. The project will also include further developments in the discrete setting, both in developing the theory at a finer level and in further applications. 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

With the support of the Chemistry of Life Processes Program in the Chemistry Division, Dr. Ao Ma from the University of Illinois Chicago is studying how proteins bind small molecules, a process both common and critical in biology and medicine. In this process of ligand binding, the conformation of proteins changes. This project aims to understand how the conformational changes are coupled to ligand movement and how the changes are also affected by water molecules. The research will use novel computational methods to reveal the step-by-step pathways and driving forces behind ligand binding. The work will help advance the understanding of enzyme function and inform the design of more effective drugs. The project will also enhance education and training through graduate curriculum development, participation in an undergraduate research program, and mentorship of high school students. The proposed research will apply the energy flow theory and generalized work functional method to compute true reaction coordinates and directly simulate the unbiased, natural dynamics of ligand binding. This approach will enable a rigorous dissection of the molecular mechanism of binding. The project will focus on two model systems: HIV-1 protease and PDZ domains, which represent the two prevalent binding paradigms-conformational selection and induced fit. Key factors that determine binding free energies and rate constants will be identified. Information will be generated on how protein conformational transitions and water dynamics collectively control ligand binding. The methods and resulting insights will advance understanding of protein-ligand interactions and support research across molecular biophysics, drug discovery, and protein engineering. 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

PROJECT SUMMARY / ABSTRACT Neutrophils are essential for host defense during bacterial infections, but their excessive activation can also promote tissue injury. Recent studies suggest that there are distinct subsets or states of neutrophils that allow them to engage in both exacerbation of inflammatory injury as well as repair and regeneration during the post-injury phase. Chemokine receptors such as CXCR2 and CXCR4 are expressed differentially on selected neutrophil subpopulations and are thought to regulate the trafficking of neutrophils between the bone marrow and the tissues during acute infections. However, the precise dynamics of how distinct neutrophil subpopulations are trafficked and how they contribute to discrete phases during lung injury and repair remain unclear. Identifying the distinct roles of CXCR2 and CXCR4 receptor signaling would allow for precision therapeutic targeting of neutrophil subpopulations in order to allow for their necessary host defense function during lung injury, minimize tissue injury caused by maladaptive neutrophil subpopulations and also enable resolution and repair of lung tissue in the post-acute phase. We have designed novel peptides that allosterically target CXCR2 and CXCR4, and our Supporting Data also suggest novel molecular mechanisms by which these chemokine receptors signal. We have furthermore studied phenotypes of activated neutrophils and assessed the dynamics of distinct neutrophil populations in the lung following inflammatory lung injury. Based on our provocative Supporting Data, we have formulated the overarching hypothesis that neutrophil subpopulations can be therapeutically targeted during key phases of inflammatory lung injury to minimize lung injury without compromising host defense or lung repair. We propose the following specific aims: In Aim 1, we will define the dynamics of neutrophil subpopulations in the bone marrow, blood and lung during the progression of injury using lung injury models and targeted genetic interventions to study the roles of neutrophils during distinct phases of lung injury and repair. In Aim 2, we will define the receptor clustering and signaling mechanisms for the chemokine receptors CXCR2 and CXCR4 in neutrophils. In Aim 3, we will assess the therapeutic efficacy of targeting neutrophil subpopulations during discrete phases of inflammatory lung injury.

NSF Awards · FY 2025 · 2025-09

This project develops a new framework for digital twin modeling of Alzheimer’s disease (AD), combining clinical data, biomedical research, and advanced computational methods to support personalized medicine. A digital twin is a computational replica of an individual’s health state, enabling the prediction of disease progression and the evaluation of treatment options tailored to the patient. The project contributes to national efforts in healthcare innovation by addressing the urgent need for a better understanding, prediction, and treatment of Alzheimer’s disease, which affects millions of Americans. This work also advances the broader field of personalized medicine by demonstrating how digital twin tools, powered by large language models, machine learning, and causal inference, can accelerate discovery and improve health outcomes. In addition, the project supports interdisciplinary collaboration across artificial intelligence, mathematics, and medicine, while offering new training opportunities for students in data science, modeling, and biomedical research. This project builds a unified modeling framework for population-based and personalized digital twins of AD. The approach uses large language models (LLMs) to extract causal networks of AD biomarkers from scientific literature and combines this with clinical data to generate personalized predictions. Conformal prediction techniques are applied to quantify uncertainty in model outputs, and optimization under limited data is achieved by integrating gradient-based learning with LLM-guided parameter search. The digital twin models simulate disease trajectories and support digital clinical trials. Treatment planning is formulated as a Markov Decision Process and solved using deep reinforcement learning to identify optimal, individualized therapeutic strategies. The framework integrates causal modeling, machine learning, generative AI, and decision theory, advancing both the science of Alzheimer’s disease and the computational tools for biomedical digital twin development. While centered on AD, the methods are generalizable and contribute broadly to AI-enabled modeling under data constraints in biomedical research. This award by the Division of Mathematical Sciences in the Mathematical and Physical Sciences Directorate is jointly supported by the Office of Advanced Cyberinfrastructure in the Computer and Information Science and Engineering Directorate. 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.