University Of California Los Angeles

NIH Research Projects · FY 2025 · 2025-11

Project Summary/Abstract Depressive symptoms represent a serious challenge to youth mental health. There is therefore an urgent need for the identification of possible mechanisms underlying risk for youth depressive symptoms. This is especially crucial for certain high-risk populations, such as youth with a history of adversity exposure. Dysregulation of the oral microbiome, the community of microorganisms inhabiting the human oral cavity, may function as a mechanism underlying risk for depressive symptoms in youth. The oral microbiome is a compelling putative mechanism for youth depressive symptoms because it is manipulable via non-invasive interventions, such as probiotic supplementation, while, at the same time, remarkably resilient to insults once it has stabilized in early adulthood. Indeed, oral microbiome dysregulation has been linked to depressive symptoms, experimentally in animal models and observationally in human youth. However, in order for potential mechanisms underlying this link to be elucidated, there is a need for research that examines the oral microbiome and depressive symptoms longitudinally, that examines the microbiome at a functional level, and that incorporates neuroimaging to better understand depressive symptom etiology. The current project will address these gaps by leveraging a 3-year longitudinal study of youth, ages 6-16 at the first timepoint (N=152), with the first 2 timepoints completed and the 3rd underway. This project oversamples for adversity-exposed youth (N=66), a population at increased risk of both depressive symptoms and oral microbiome dysregulation. Oral microbiome composition and depressive symptoms will have been assessed at all three timepoints, and functional Magnetic Resonance Imaging (fMRI) conducted at the final timepoint. We will analyze the relationship between the oral microbiome and depressive symptoms, and the relationship between the oral microbiome and functional brain connectivity. We hypothesize that elevated pathogenic taxa, increased pro-inflammatory functions of the oral microbiome, and decreased aromatic amino acid precursor biosynthesis will be associated with increased depressive symptoms. We further hypothesize these same indicators of oral microbiome dysregulation will also be associated with altered functional brain connectivity, especially within the affective limbic network, reward network, default mode network, and cognitive control network. This project’s findings will yield critical understanding about potential peripheral mechanisms underlying depressive symptoms in both typically developing and high-risk youth.

NSF Awards · FY 2025 · 2025-11

Artificial intelligence (AI) systems that read clinical notes and medical images promise earlier diagnoses, personalized treatments, and lower costs. However, these systems face critical challenges that threaten their reliability and ethical use. Data integrity problems, such as mistakes or tampering, can distort models and endanger patient care. In addition, patient data may be withdrawn due to revoked consent or legal obligations. There is no reliable way to see where a medical model's training data came from, whether that data was tampered with, or how to delete patient records effectively and efficiently from a model. This project will create the first end-to-end provenance framework for medical AI that enables tracing, auditing, and, when necessary, removing data efficiently and efficiently. The results will improve patient privacy, reliability of medical AI, and provide open-source tools for trustworthy AI. Building this framework is challenging, as medical datasets are unstructured, multimodal, dynamic, and come from many providers. Further, removing tainted data can force a full model retrain and thus harm model performance. To address these challenges, this research has three thrusts: (1) Automated inferences of public machine learning (ML) models that were trained on corrupt datasets, (2) Efficient logging of datasets and ML models usage in medical research workflows, and (3) Efficient machine unlearning to remove compromised or sensitive data points without retraining. This project advances the foundations of secure medical data provenance, machine unlearning, and provides open-source tools and coursework that prepare the next generation of medical and computer scientists to build trustworthy AI. 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

Extreme weather events—such as heat waves, cold snaps, wildfires, and heavy rainfall—pose increasing risks to society. These events are often driven by shifts in powerful atmospheric jet streams, which typically flow from west to east across the midlatitudes but can sometimes meander dramatically north or south. While recent advances in AI have shown promise in improving weather forecasts, many AI models still struggle with long-term stability, limiting their effectiveness in predicting extreme events. This project seeks to address that challenge by combining physics-based atmospheric science with state-of-the-art machine learning techniques. The goal is to enhance our ability to forecast high-impact weather events, ultimately supporting more effective planning and response in sectors such as energy, transportation, water management, and public health. Additionally, the project will contribute to education and workforce development. Graduate students will receive advanced training in both physics-based and data-driven approaches to atmospheric modeling, while several undergraduate students will gain hand-on experience in applying statistical analysis and AI techniques to weather data. The models and tools developed through this research will be made publicly available, fostering collaboration across the broader scientific community. The proposed research will advance understanding of atmospheric circulation and extreme weather by integrating a hierarchy of stochastic models with large-scale atmospheric dynamics. This hierarchy will include both deep learning-based generative models and traditional linear and nonlinear inverse models. The latter will serve as rigorous benchmarks to evaluate the long-term stability and reliability of the AI-based approaches, particularly in capturing the full probability distribution of midlatitude weather systems. Using these stochastic models, the project will generate large ensembles of simulated weather events, with a focus on extreme events and their subseasonal precursors. The research will also investigate the influence of stratospheric and tropical processes on midlatitude circulation, as well as the dynamic coupling between atmospheric flow and water vapor, using both linear and nonlinear frameworks. By bridging machine learning and atmospheric dynamics, this work strives to improve the performance of AI-based forecasting systems and contribute to a more comprehensive understanding of large-scale atmospheric variability. 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

Quantum sensors capitalize on the strong sensitivity of quantum systems to external disturbances to measure various physical phenomena with extreme precision. Quantum sensing is a rapidly emerging field with many applications, including detecting gravitational and magnetic fields, biological measurement, imaging, etc. The full potential of quantum sensing is realized by using a distributed network of quantum sensors to estimate physical phenomena; in particular, when a network of quantum sensors allows the sensors to be in an entangled (correlated) state, its precision is further improved. There has been recent work using multiple quantum sensors, but the use of a distributed network of quantum sensors working collaboratively to estimate complex physical phenomena, as in many classical sensor network applications, has remained largely unexplored. This proposal seeks to fill this gap and investigate many scientific challenges that arise in developing efficient sensing protocols for a quantum sensor network (QSN). In addition, the project helps develop the workforce in this emerging quantum sensing and communication field by designing and offering educational programs targeting a wide variety of students ranging from those in high school to those in graduate school and beyond. The goal of this project is to tackle the main challenges and problems that arise in building QSNs. The research work consists of the following thrusts: (i) Initial State and Measurement Optimization. The initial state of the quantum sensing system can strongly affect the estimation error of the sensed parameter. Thus, the project will investigate the optimization problem of determining the optimal initial state and global measurement that minimizes the estimation error. (ii) Event Localization Schemes via QSNs. The project will design efficient schemes for event localization using QSNs. (iii) Distribution of Quantum Circuits in QSNs. The project will develop techniques for the distributed implementation of quantum sensing circuits in QSNs, with an objective to minimize aggregate quantum error or execution latency of distributed circuits. (iv) Declarative Framework for Specifying QSN Protocols. The project plans to develop a programming framework for specifying, evaluating, and reasoning over QSN protocols. (v) QSN Simulator, System Evaluation, and QSN Testbeds. The project will develop a QSN simulator that can be used to simulate general QSN applications. In addition, the developed techniques will be evaluated over three platforms: large-scale simulations over the QSN simulator, moderate-scale simulations over a cloud quantum computer, and small-scale experiments on two QSN testbeds. 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

The study of memorization in Artificial Intelligence (AI) models has become increasingly important as AI has become more widely adopted in society. This phenomenon presents significant risks, including potential privacy violations, copyright infringement, and failure to generalize beyond the limited training data. Notwithstanding its critical importance, there is a lack of understanding of the factors underlying memorization in AI, and without such understanding, the memorization problem cannot be systematically addressed, managed or mitigated. This project will develop foundational theory that will elucidate the mathematical, statistical, and contextual principles that affect memorization in AI, culminating in the development of reliable, robust, and privacy-preserving AI models. A unique aspect of the project's research framework is that it combines optimization theory, dynamical systems theory, and information theory to develop useful insights into the interplay between memorization, generalization, privacy, reproducibility, and model robustness. These insights will lead to the development of AI models that are less susceptible to privacy violations, model overfitting, and adversarial attacks, thereby enhancing the trustworthiness and applicability of AI across many domains. The investigators will integrate the research into the curriculum, engage undergraduate students in research, and hold workshops to foster collaboration and share our findings with the wider academic and professional communities. The research in this project centers around several interconnected themes that provide a systematic and innovative framework for studying the memorization phenomenon in AI. The first theme is the development of computationally efficient and principled metrics for quantifying memorization in AI models. The project will leverage harnessing memorization proxies for various learning objectives, including machine unlearning and deduplication. The second theme is the mathematical characterization of performance tradeoffs and dependencies between memorization, generalization, and the replicability of large AI models in diverse settings. Based on such findings and characterizations, the research will lead to the development of mechanisms to prevent overfitting while ensuring efficient learning and generalization to practical scenarios where the training and test data contain frequent outliers governed by long-tailed data distributions. The third theme is validation of the project's theory and methods on important practical AI applications, including multitask inference of dynamical systems, heterogeneous and federated learning problems, and understanding incentives in human decision making. 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

ABSTRACT: The Young Homotopy-theorist Meeting will be held at the International Centre for Mathematical Sciences (ICMS) in Edinburgh, Scotland, October 6 - 10, 2025. The workshop will bring together 35 early-career mathematicians to facilitate collaborative research and training. The workshop will provide an unparalleled opportunity for young researchers who use the tools of homotopy theory to develop long-lasting and meaningful international collaborations. The award will allow US-based researchers to be involved in this unique opportunity. The organizers have secured funding for the conference through the ICMS covering most conference costs and this National Science Foundation grant will specifically fund travel to the conference for US-based mathematicians. Participation of US-based mathematicians will be beneficial to the progress of not only homotopy theory research in the US, but also to mathematical progress in many adjacent disciplines. Homotopical techniques originated in algebraic topology and homological algebra, but in recent years more broad applications of homotopy theory abound. For example, homotopical tools and ideas play a major work in the influential work of Bhatt--Morrow--Scholze on integral p-adic Hodge theory; work of Scholze–Clausen on condensed mathematics; work of Hill--Hopkins--Ravenel on the Kervaire invariant one problem; and a resolution of a conjecture of Grothendieck by Carlson–Haine–Wolf. These examples highlight the timeliness and importance of promoting research collaborations amongst young homotopy theorists. With this in mind, the conference will be organized to provide a research collaboration launching pad for participants. The conference will feature thirteen talks by participants, providing an opportunity for participants to share and discuss their work to their peers. Amongst the speakers are the four invited early-career speakers: Robert Burklund (Copenhagen), Peter Haine (Berkeley), Alice Hedenlund (Uppsala), and Lucy Yang (Columbia). The conference will also include two mini-courses by Clark Barwick and Piotr Pstrągowski. The mini-course topics will bring participants to the forefront of these active research areas, creating a pathway to engage new scholars. There will be two evening lightning sessions, where participants can give ten- to fifteen-minute talks showcasing their research, and there will be a poster session on Monday. The conference schedule will also allow ample time for informal discussion. Information about the conference may be found at https://www.icms.org.uk/YoungHomotopyTheorists 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

Linguists typically study the sounds of human languages from two different perspectives: the phonological one, which focuses on their patterns and organization at a symbolic level (e.g., language X has more vowels than language Y) and the phonetic one, which focuses on physical properties, including acoustic characteristics (e.g., intensity) and articulatory movements (e.g., the sound /b/ is produced by closing the lips). An ongoing debate on the realization of consonant sounds is whether the amount of effort speakers make to produce them is represented for different sounds at an abstract level in the mind or is simply explained by the position these consonants occupy in words and sentences. This project sheds light on this matter by analyzing and comparing the acoustic properties of multiple consonants in different positions in two languages. Additionally, this study trains and supports student participation, contributing to the development of the next generation of language researchers. All sound productions require some degree of movement in the vocal tract, and languages generally show two processes that regulate the amount of effort involved in those movements, namely fortition (more articulatory effort and clearer gestures) and lenition (less effort and more relaxed movements). This study tests a) what kinds of consonants are affected by these two processes, b) how these two processes are conditioned by the position of the consonant (e.g., beginning of a word vs. between vowels vs. beginning of a phrase), and c) if some common sound patterns involving these two processes are predictable from the duration of different sounds. The analysis of two different languages also serves as a starting point for a cross-linguistic comparison leading to a greater understanding of the extent to which linguistic sound patterns are dependent on abstract mental processes or concrete physical ones. 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

As there is no cure for Alzheimer's Diseases and Related Dementias (ADRD), it is imperative to invest in ways to prevent them before they occur. Prior studies have found disparities in ADRD prevalence based on socioeconomic status (SES), race/ethnicity, and sex, with the highest prevalence observed in those who are Black, Hispanic, women, or living in low-SES households. However, few studies have examined the impact of biological factors such as inflammation, Hemoglobin A1c and none have quantified their impact on SES, race/ethnicity and sex disparities in dementia. Furthermore, few studies have considered whether psychosocial factors modify the association between lifestyle/metabolic factors and dementia risk, or whether these associations differ based on SES, race/ethnicity or sex. A deeper understanding of the complex interrelationships between lifestyle, metabolic and psychosocial factors on dementia risk is needed to develop effective interventions to reduce disparities in dementia. Lastly, when designing interventions to prevent ADRD, policymakers must consider inherent trade-offs between interventions that maximize health in the general population and those that minimize health disparities. For dementia, the potential benefits, cost-effectiveness and tradeoffs among these health approaches in reducing disparities are as yet untested. The proposed study, Personalized Targeted Multi-Factorial Approaches to Reducing DIS-parities in ADRD (TARDIS-AD project), will leverage our team’s expertise in causal mediation, machine learning, and simulation modeling to identify personalized intervention strategies that would most effectively and cost effectively reduce disparities in dementia risk. Specifically, the proposed study aims to: Aim 1: Determine the extent to which (a) lifestyle and (b) metabolic factors mediate the relationships of SES, race/ethnicity, sex, with dementia. We hypothesize that SES-, race/ethnicity-, and sex disparities in dementia risk are partially explained by differences in lifestyle (e.g. diet) and metabolic factors (e.g. Hemoglobin A1c) and thus targeting these lifestyle and metabolic factors could help reduce disparities in dementia. Aim 2: Identify early- and midlife psychosocial factors (e.g., early-life trauma) that modify the effect of lifestyle and metabolic factors on dementia risk in vulnerable subpopulations (e.g. low-SES). We hypothesize that negative psychosocial experiences (e.g. early-life trauma) suppress the effect of protective lifestyle/metabolic factors (e.g. high exercise level) on dementia risk in vulnerable subpopulations, suggesting that interventions will need to identify and address these underlying issues to effectively address dementia risk. Aim 3: Evaluate (a) the potential sustained effects of lifestyle/metabolic interventions on dementia incidence targeting high-risk subpopulations, the general population and vulnerable subpopulations, and (b) the cost-effectiveness of these interventions. We hypothesize that Interventions targeting vulnerable subpopulations can yield greater reduction of dementia disparities and be more cost-effective than the general population or high-risk approach.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY There are no widespread devices available that significantly improve the quality of life for those with paralysis. A widespread and effective device for paralysis should be low risk, yet high performance, enabling complex movements needed to perform everyday tasks. We propose to achieve a low risk, high performance device through three thrusts. Our first thrust is to capitalize on advances in artificial intelligence (AI) that enable us to train robots that perform the same types of tasks that humans perform, including folding laundry, putting food in a pan, opening door knobs, or picking up a bag of chips. Because robots can perform these tasks, we reason that as long as an AI copilot can infer a human's goal to perform a certain movement, the copilot can then help perform the complex movements associated with the task. We achieve this by training new copilot architectures that use computer vision, as well as non-invasive signals reflecting the user's intent, to perform complex actions. Our second thrust is to take advantage of and fuse many non-invasive signal sources, which together provide enough information for the copilot to infer the user's goal and help the user complete an action. Our third thrust is to develop methods to adapt the user and copilot as they share control. We hypothesize this will have a key impact on increasing system performance and robustness. We believe this proposed work, if successful, will have a transformative impact on the development of clinically viable devices that help people with paralysis move autonomously.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Alzheimer's Disease and Alzheimer's Disease Related Dementias (AD/ADRD) are leading causes of morbidity, mortality, and excess healthcare costs. Care for AD/ADRD patients requires multiple providers, often hindered by fragmented communication. As treatment options continue to expand and an aging population with frequent comorbidities, challenges in care coordination will multiply and represent an urgent need in healthcare. In our prior work we have found that digital tracings of communication network structures identified within the Electronic Health Record (EHR) predict cancer patients’ outcomes (e.g., ED visits, mortality), hence we are developing scalable EHR tools to improve cancer patients’ care. Our results in cancer provide a strong foundation for studying AD/ADRD patients, given similarities in the complexity of care coordination across multiple providers and specialties. This study of persons with AD/ADRD expands on our prior work in cancer by factoring in the longer-term nature of AD/ADRD patient care and adding a significant new dimension to the care team: patient and caregiver communications via EHR patient portals. We will leverage social network analysis, machine learning (ML)-assisted visual analytics, and qualitative methods to study EHR communication structure data at three University of California health systems that all use Epic. Pilot analyses identified N=30,523 AD/ADRD patients with over 2.7 million patient/caregiver portal messages and 108,709 healthcare providers asynchronously accessing their digital records. We focus on one modifiable dimension of team communication, information sharing via EHRs. Aim 1: Develop the first multisite database of EHR multiteam system communication in AD/ADRD care, incorporating novel social network measures to describe and quantify within- and between-group collaborative patterns in intricate EHR interactions. Aim 2: Analyze the association between targeted EHR communication structures and quality outcomes through social network analyses, focusing on preventable ED visits and unplanned hospitalizations in AD/ADRD care. Aim 3: Utilize machine learning-assisted techniques to characterize and assess collaborative care dynamics, identify EHR communication structures associated with poor quality outcomes, and predict patient outcomes and events. Aim 4: Apply qualitative methods to deepen our understanding of the findings from Aims 1-3, exploring how and why communication structures and teaming patterns impact collaborative care delivery. Multisystem healthcare teams and communication structures need to be understood and then designed much more intentionally and based on evidence. Our innovative methods will provide data to help us improve our fragmented healthcare system and ensure quality care.

NIH Research Projects · FY 2025 · 2025-09

ABSTRACT The prevalence of autism spectrum disorder (ASD) has dramatically increased in recent years to a rate of 1 in 31 U.S children. ASD risk is multifactorial, with genetic and environmental elements each playing a role. While major progress has been made in identifying causal genetic variants, environmental— particularly prenatal—exposures are increasingly recognized as critical contributors to neurodevelopmental risk. Over 200 high-production volume chemicals are routinely detected in American adults, including pregnant women. The ways in which environmental factors contribute to ASD risk (and how they interact with genetic risk) remain unclear, however. Understanding how these environmental chemicals influence neurodevelopment at the cellular and molecular level is crucial for development of mitigation strategies. Progress has been hampered by lack of large-scale data assessing the impact of chemicals at a cellular and molecular level in human neural cells that faithfully model early human brain development. A major barrier to progress has been the absence of scalable experimental platforms capable of evaluating compound exposures in ASD-relevant cell types and with comprehensive molecular readouts . Most existing studies rely on immortalized cell lines, which do not faithfully model human brain development. Here, in this project: “Data science approaches to autism: environmental modulators of the transcriptome and gene-x-environment interactions,” we address this substantial gap in the field by capitalizing on recent advances in genomics, stem cell technology, and data science. We will assess the activity of thousands of chemicals in the primary cell types implicated by ASD genetic risk, human neural progenitor cells and neurons. We leverage human stem cell-based model systems, which have been shown to accurately and reliably model key aspects of human brain development. We couple this with high-throughput culturing and robotic systems, which provide a unique opportunity to efficiently screen the entire ToxCast II/III library of ~4,700 chemical compounds to which humans are exposed, in both human primary neural progenitors (phNPCs) and induced neurons (iNeurons). We use an unbiased, genome-wide measurement at the cellular level, single-nucleus RNAseq, to comprehensively understand the effects that chemical exposures have on human neural development and to study the impacted gene regulatory networks and pathways. We will integrate this with public exposure data and molecular profiling in the human brain to find overlapping pathways. We will then evaluate how genetic background modulates cellular responses by using cell villages to model gene–environment (GxE) interactions across a cohort of 150 donors, including 110 individuals with ASD and 40 neurotypical controls. Using this framework, we will map exposure- responsive expression quantitative trait loci (eQTLs) for more than 40 chemicals with a novel method developed by one of the PIs, enabling the identification of GxE interactions at scale. We will integrate these data with publicly available data sources (e.g. PsychENCODE and ECHO). We will model the biophysical interactions between relevant chemicals and proteins, to provide molecular insights into the underlying mechanisms of action – namely, the means by which compounds induce systemic gene expression via perturbations in gene regulatory networks. We will build a public resource that will enable further mechanistic investigation. Our project will: 1) provide insight into mechanisms of action underlying known autism risk factors, 2) identify novel chemical risk factors that impact essential pathways in early brain development, and 3) query how genetic factors modulate susceptibility and resilience to chemical exposures, which will be comprehensively integrated with existing databases and resources.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY ABSTRACT During menopause, estrogen levels decline, and this decline is accompanied by a myriad of metabolic changes, including decreased energy expenditure. The signaling mechanisms underlying the effects of estrogen on energy expenditure remain incompletely understood, however. In response to energy deficit, mice have evolved the ability to drastically reduce their energy expenditure by entering a hypothermic and hypometabolic state known as torpor. From my preliminary research, it has been shown that circulating estrogens reduce fasting-induced torpor usage, keeping energy expenditure high even in the face of a dangerous energy deficit. The proposed research seeks to understand the signaling mechanisms underlying this estrogenic remapping of the metabolic response to an energy deficit through two major aims. 1) Test whether circulating estrogens reduce torpor usage by acting on the medial preoptic area of the hypothalamus, a brain region that is sensitive to circulating estrogens and is known to coordinate torpor in mice, making it an ideal candidate. 2) Test whether circulating estrogens alter the activity of neurons in the medial preoptic area of the hypothalamus, directly or indirectly, which could represent a critical cellular mechanism underlying the effects of circulating estrogens on torpor. Together, these studies will advance our understanding of how estrogen influences metabolic pathways in mice, an important step towards understanding, and eventually treating, the metabolic changes that occur during menopause.

NIH Research Projects · FY 2025 · 2025-09

MEDIC – MicroED Imaging Center at UCLA PROJECT SUMMARY The MicroED Imaging Center (MEDIC) at UCLA is a current Biomedical Technology Research Resource center devoted to the advancement and dissemination of the Microcrystal Electron Diffraction (MicroED) method. MicroED facilitates the determination of new previously unattainable structures at atomic resolution from vanishingly small crystals. MicroED exploits the strong interaction between electrons and nano-scale three- dimensional crystals and takes advantage of emerging cryo-EM instrumentation coupled to established crystallographic methods. We pioneered MicroED and established the current highly successful and innovative center in 2020. Since then, MEDIC trained over 1000 individuals, led to the establishment of robust pipelines and methods for sample preparation, data collection and reduction; provided sorely needed software to the community (which was downloaded >15000 times) and disseminated results in social media, workshops, peer reviewed papers and commentaries that collectively have been downloaded >83,000 times. MEDIC was heavily involved in dissemination of MicroED and training the next generation of practitioners through our highly successful community engagement programs. MEDIC collaborated with key technology partners to develop MicroED systems and currently two leading manufacturers are offering MicroED solutions based on the technologies we developed. We are proposing to transition the highly innovative and impactful MEDIC center to a Biomedical Optimization and Dissemination (BTOD) center. We outline comprehensive plans for MEDIC administration, three Technology Optimization and Dissemination projects (TOPs), applications through center selected Driving Biomedical Projects (DBPs), and a comprehensive Community Engagement effort, including several technology partnerships. The TOPs are focused on (1) Enhancing macromolecular MicroED through optimization of sample preparation methods, (2) Optimizing MicroED applications in chemical crystallography and drug discovery, and (3) transforming MicroED into a high throughput method through optimized automated data collection and processing procedures. These TOPs will directly interact with the carefully selected DBPs, which present unique and challenging samples that are impossible to study with any other approach. These DBPs will allow us to further optimize and refine the approaches in the TOPs. A strong and innovative community engagement program, including a new MEDIC Scholars Program, will help disseminated the technologies and provide training opportunities across all career stages and promote MicroED research in institutions located in IDeA states. MEDIC will continue its impactful work to ensure continued optimization of MicroED and dissemination of the method. MicroED will be democratized through innovative and extensive user training and support, and work with technology partnerships. Taken together we expect MEDIC to continue playing a pivotal role in optimization of MicroED technologies and dissemination to bring new and important structures to light.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Older adults with gastroesophageal cancer (GEC) face a significant challenge: treatments that extend life can come at the cost of reduced physical function, which can diminish independence and quality of life. Indeed, GEC treatments are often aggressive, involving systemic chemotherapy, chemoradiotherapy, and/or immunotherapy, which can be particularly taxing on older patients. There is a pressing need to understand how these treatments affect key outcomes like physical function to better support this vulnerable population. Despite this, no study has prospectively evaluated how GEC treatments affect physical function or identified risk factors for decline. Moreover, no study has explored the lived experiences of older adults undergoing these grueling treatments, the challenges the older patient faces in maintaining physical function during treatment, or their perceptions of whether treatment was worthwhile upon completion. This study aims to fill these critical gaps by prospectively evaluating how GEC treatments impact physical function in older adults, identifying the barriers and facilitators for maintaining function during treatment, and eliciting patient perceptions of treatment worthwhileness. To do this, I will conduct a prospective cohort study of older adults (aged ≥60 years) with GEC starting systemic treatment (i.e., chemotherapy, chemoradiotherapy, and/or immunotherapy). Participants will undergo serial geriatric assessment prior to starting treatment (baseline) and at 2, 4, and 6 months, which will include measures of physical, cognitive, and psychological function. The primary outcome will be the change in physical function, measured using the Short Physical Performance Battery, from baseline to 6 months. Secondary outcomes will include change in other measures of physical (grip strength, TUG, ADL/iADL), cognitive, and psychological function as well as frailty. The change in these measures will also be evaluated from baseline to 2 and 4 months. Additionally, I will perform a qualitive sub-study, where I will invite 20-30 participants, using stratified sampling, to participate in semi-structured interviews at baseline and 6 months to explore how treatment affected their function and elicit treatment worthwhileness. Aim 1 will assess the patterns and determinants of physical function decline. Aim 2 will explore the barriers and facilitators of maintaining physical function, focusing on motivation and prioritization of health. An Exploratory Aim will capture patients’ reflections on the worthwhileness of treatment, particularly in relation to its impact on function. By the end of this study, I will generate both quantitative and qualitative data to offer a comprehensive understanding of how GEC treatments affect physical function in older adults. These findings will not only inform personalized, patient-centered treatment decisions but could also guide the development of targeted interventions aimed at optimizing care for this vulnerable population. Ultimately, this work will provide the foundation for my NIA K76 application to pilot test a rehabilitation intervention designed to help older adults with GEC maintain physical function during and after systemic treatment, shifting our focus in oncology from survival alone to ensuring that patients can live robust lives after treatment.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY The purpose of this study is to test the clinical effectiveness of the Research Units on Behavioral Interventions in Educational Settings (RUBIES) program relative to educator psychoeducation on externalizing behaviors of autistic children in public elementary schools as well as the effects of an organizational implementation strategy, Helping Educational Leaders Mobilize Evidence (HELM), versus an implementation attention control on RUBIES sustainment. The increased prevalence of autism spectrum disorder (1 in 36 children) in the United States along with the exorbitant cost of care of supporting one autistic individual with and without intellectual disability across their lifespan ($2.4 and $1.4 million, respectively) creates a sense of urgency to improve outcomes for autistic children. Publicly funded education systems are the primary setting in which autistic children receive services; however, behavior management strategies that educators can use for this population often are extremely time-consuming and resource-intensive. Schools carry unique service delivery opportunities and constraints that necessitate careful evaluation of programs and practices in that context. There is a dire need to support schools to build systems and structures to support implementation with fidelity and sustainment over time. To address this, we propose a cluster-randomized, hybrid type 2 effectiveness-implementation trial with schools randomized to: 1) educator coaching in RUBIES and school participation in HELM; 2) educator coaching in RUBIES and school participation in an implementation attention control (IAC) condition; or 3) a control arm incorporating an active clinical comparator, educator psychoeducation. We will enroll 40 schools, 120 educators, and 120 autistic children. We aim to: 1) test the clinical effectiveness of RUBIES, relative to educator psychoeducation, on externalizing behavior of autistic children in public schools; 2) test the effects of an organizational implementation strategy (HELM) versus implementation attention control (IAC) on RUBIES sustainment; and 3) evaluate the mechanisms of RUBIES and HELM on child and implementation outcomes. The proposed work directly responds to high priority research areas of the US Department of Health and Human Services Interagency Autism Coordinating Committee’s Strategic Plan for Autism Research, which calls for expanded research on the translation of proven-efficacious interventions into the community, NIMH Strategic Priority 3.3 to test interventions for effectiveness in community practice settings, and NIMH Strategic Priority 4.2 to expedite adoption, sustained implementation, and continuous improvement of evidence-based mental health services. If successful, this study will have substantial public health impact because it will produce an effective intervention for a prevalent problem among a high impact population in schools across the USA and will determine how to sustain this (and other) intervention(s) with high fidelity, to the betterment of health.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Brain asymmetry, a conserved feature of the vertebrate nervous system, is thought to be an advantageous mechanism for efficient information processing. This asymmetry manifests in anatomical and molecular left-right (L-R) differences that correlate with specialized functions in specific cognitive processes. Disruption of asymmetry in multiple brain regions including the prefrontal lobe and the amygdala has been implicated in the pathogenesis of several neurodevelopmental disorders, such as autism spectrum disorders. In mammals, brain asymmetry has been demonstrated in multiple brain regions involved in regulating emotion processing and memory, including the habenula, the hippocampus, the prefrontal cortex, and the amygdala. Our preliminary single-cell RNA sequencing (scRNAseq) and in situ hybridization (ISH) analyses revealed a distinct, left-to-right biphasic expression pattern of the early B cell factor gene, Ebf1, in the lateral ganglionic eminence (LGE)/striatum and central amygdalar nucleus (CeA). Ebf1 expression displayed a predominant L-sided dominance in the LGE/striatum and CeA before embryonic day 14 (E14), switching to R-sided dominance after E16 and persisting into adulthood. Furthermore, our ISH data showed that Ebf1 primarily colocalizes with Drd1 in striatal and CeA cells, suggesting a role for Ebf1+ cells in regulating the acquisition of contextual fear, Pavlovian conditioned fear responses, and appetitive behaviors. In this proposed study, I aim to investigate the cellular and molecular signatures of Ebf1 cells in the amygdala and determine whether these lateralized expression patterns have functional consequences in regulating emotion-related behaviors. By forging a new path in this area of research, our findings have the potential to significantly advance our understanding of neurodevelopmental disorders. To achieve this goal, we have outlined three specific aims: 1) To trace the developmental origin of asymmetrical Ebf1+ cells and their progenies in the L-R CeA. 2) To identify the cellular and molecular signatures of asymmetrically distributed Ebf1+ cells and Ebf1 lineages in adult CeA. 3) To investigate how asymmetrical Ebf1+ cells contribute to CeA-dependent emotion behaviors. This study will address the following key questions: When do the asymmetric L-R Ebf1 cells develop in the CeA? What are the functional properties of CeA-Ebf1 cells? How do these unilaterally distributed Ebf1 cells contribute to behavioral asymmetry? By elucidating these questions, this study will serve as a proof-of-principle, paving the way for a deeper understanding of brain asymmetry and its potential role in neurodevelopmental disorders.

NIH Research Projects · FY 2026 · 2025-09

Summary/Abstract This project aims to explore how genetic variation influences disease progression and therapeutic response in age-related macular degeneration (AMD) by integrating advanced optical coherence tomography (OCT) imaging with machine learning (ML) and artificial intelligence (AI). AMD, a leading cause of vision loss in older adults, presents with highly variable progression, ranging from slow decline to rapid vision loss. This variability remains poorly understood, largely due to the genetic and phenotypic diversity of AMD. This project leverages ML for deep phenotyping of OCT data to refine the classification of AMD subtypes, combined with genetic analysis for a deeper understanding of disease progression. The hypothesis is that genetic variations influence distinct AMD subtypes and stages, shaping both disease progression and therapeutic response. To test this hypothesis, the following aims are proposed: Specific Aim 1: Determine how genetic variation influences AMD progression using AI-driven analysis of OCT biomarkers and ML to classify patients as slow or rapid progressors. Three-dimensional ML models, such as SLIViT and retina-specific models like RETFound, will enable detailed phenotype analysis, revealing high-risk features and novel subtypes correlated with progression rates. Specific Aim 2: Investigate how genetic variation affects functional and therapeutic outcomes in AMD by integrating OCT data with patient treatment responses through ML-based models, exploring genetic factors linked to visual acuity outcomes and treatment efficacy. The project will refine polygenic risk scores (PRS) by combining these insights with genome-wide association study (GWAS) data to improve predictions of rapid progression and treatment responsiveness. Datasets from the UK Biobank and UCLA Biobank will be utilized, applying ML-based imaging analysis, transfer learning for 3D data, ML-based deep phenotyping, and traditional and post-GWAS analysis. Techniques include ML-based progression analysis (e.g., pySuStain) and causal ML for treatment response. This research aims to identify novel genetic loci associated with AMD subtypes, offering new insights into disease mechanisms and potentially unrecognized pathways. These findings will enhance PRS models, enabling better stratification of patients by genetic risk and advancing personalized approaches to AMD management. Supported by a team of mentors with expertise in retinal imaging, genetics, and bioinformatics, this project seeks to impact public health by guiding diagnosis, therapeutic, and prognosis to reduce vision loss in patients with AMD.

NIH Research Projects · FY 2025 · 2025-09

Abstract Autonomic dysregulation and aberrant intracellular signaling are hallmarks of both heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF). Cyclic AMP (cAMP) is a key second messenger that transduces extracellular autonomic adrenergic signals to control downstream contractility and electrophysiology on a beat-to-beat basis. Aberrant cAMP signaling likely plays an important role in Ca2+ mishandling, contractile dysfunction, and arrhythmias in HF. cAMP is highly compartmentalized by b-adrenoceptor (b-AR)-induced cAMP synthesis and phosphodiesterase (PDE)-mediated degradation. The overall goal of this project is to determine the differential role of heterogeneous PDE4D signaling in contributing to dysfunctional excitation-contraction coupling (ECC) and arrhythmias in HFpEF vs. HFrEF in a sex-dependent manner. The main hypothesis that will be tested in this project is that either too much or too little PDE activity in cardiomyocytes can impair ECC and promote arrhythmias, and that underlying sex differences in PDE activity modulate sex-dependent responses. Using a novel integrated whole-heart imaging approach combined with single cell analysis and detailed subcellular signaling studies and FRET-based reporters, we will comprehensively determine signaling responses and functional outcomes from the molecular, cellular, to whole- heart level, which may ultimately suggest more personalized treatment strategies for men and women with HF.

NIH Research Projects · FY 2025 · 2025-09

PROJECT ABSTRACT The number of people experiencing homelessness (PEH) in the U.S. has reached record levels, rising by 13% since 2020. A growing proportion of PEH are experiencing chronic homelessness, illustrating increasing challenges in creating effective pathways to stable housing. The short supply of affordable housing in communities across the U.S. complicates efforts to scale evidence-based, permanent supportive housing programs to meet the scale of the homelessness crisis. Faced with a growing population of PEH living unsheltered in homeless encampments, many municipalities have been developing new, innovative programs focused on “encampment resolution”. Encampment Resolution Programs (ERPs) utilize harm reduction policies similar to housing first programs and provide a range of interim housing options. ERPs have been endorsed as a way to quickly clear the streets and help reduce health disparities and mortality among PEH as they transition to permanent housing. However, there is also a risk that these programs could create a revolving door, sending PEH back to the streets and worsening health disparities by severing their connections to trusted social and healthcare networks. Hundreds of millions of dollars have been allocated to support ERPs, yet there is limited empirical evidence examining their effectiveness in reducing homelessness and improving health outcomes. This proposed project will use a mixed method design to provide the first longitudinal evidence on the potential health benefits of ERPs. The study is informed by an integrated weathering framework that links the environmental stressors associated with unsheltered homelessness to indicators of accelerated aging. Our work is situated in Los Angeles County, CA, which has the nation’s largest homeless population, with 75% living unsheltered. We will recruit 400 ERP enrollees into a unique and existing longitudinal study of ~500 unsheltered PEH known as the Periodic Assessment of Trajectories of Housing, Health and Homelessness Study, or PATHS. Using monthly and biannual surveys, we will assess the effects of ERPs on long-term housing trajectories and a range of health outcomes by comparing the newly recruited ERP cohort to the existing representative comparison cohort from PATHS. Outcomes will include those related to accelerated aging, its underlying drivers, and the social determinants of health. We will examine the overall effects of the ERP program on health, and disaggregate the results based on whether individuals progress to permanent housing, remain in interim housing, or return to the streets. These findings will be complemented by qualitative interviews with ERP participants about their health and housing experiences. Based on these results, we will develop innovative housing policy guidelines to support policymakers and practitioners in improving the health and well-being of PEH.

NIH Research Projects · FY 2025 · 2025-09

Project Summary Dermatomyositis (DM) is a rare autoimmune disease, affecting predominantly women, particularly Black women, which associates with marked disease-related morbidity as well as a 10-fold increased risk of death. DM is characterized by muscle weakness and skin rashes, but over 70% of patients may also develop interstitial lung disease (ILD), referred to as myositis associated ILD (MA-ILD), a leading driver of early mortality. Our preliminary data suggests that Black women have higher quantitative lung fibrosis scores reflective of irreversible lung damage compared to White women upon specialty center referral. Our data also suggests that microvascular inflammation and impaired paraoxonase 1 (PON1) activity may play pathogenic roles in DM and MA-ILD. Improving disease outcomes and establishing treatment guidelines in a rare disease such as DM requires 1) the integrated analysis and understanding of current diagnostic and treatment practices, and 2) the mechanistic understanding of the disease to develop targeted biomarkers and therapeutics. In Aim 1 we will establish the Southern California Dermatomyositis Disease Network (SCAD), a patient centered, community myositis registry which will recruit patients with DM and MA-ILD syndromes across diverse healthcare systems with a focus on recruitment of underrepresented in medicine (URM) women. In Aim 2 we will investigate whether microvascular disease drives progression of MA-ILD using a novel approach to ILD assessment on computed tomography CT imaging and histochemical and transcriptomic analyses of lung biopsies from MA-ILD patients. We hypothesize that incorporation of a microvascular disease assessment into the current computational evaluation of ILD will more accurately identify the extent of MA-ILD and more accurately predict disease progression and functional lung outcomes compared to traditional assessments and ILD pattern analysis. We hypothesize that histologic analysis will further support microvascular disease involvement in MA-ILD and provide needed insights into disease pathogenesis. In Aim 3 we will assess PON1 activity and a panel of immune and vascular activation markers in serum collected from DM patients participating in the first placebo-controlled trial of intravenous immunoglobulin (IVIg) as well as investigate these pathways in a longitudinal DM and MA-ILD cohort. We hypothesize that the activity of PON1, a major antioxidant protein of HDL, which normally regulates systemic oxidative stress by neutralizing pro-inflammatory lipid mediators, will associate with disease activity and treatment response in women with DM and MA-ILD syndromes. The significance of this work lies in its overall goal to understand a disease that is currently markedly understudied in women, particularly Black women for whom minimal research exists. The work establishes the first community DM registry in the Western US with a focus on understanding disease in URM women and investigates novel pathways driving DM and MA-ILD, which could provide new biomarkers for disease monitoring as well as potential therapeutic targets.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with rising prevalence, currently affecting 2.8% of children in the United States, with vast disparities based on race/ethnicity, neighborhood socioeconomic status, and region. While genetic factors play a significant role in ASD, growing evidence suggests that environmental exposures, particularly air pollution (AP), contribute to ASD risk. Despite well- established links between AP and adverse health outcomes, the specific sources and components of AP, such as traffic, wildfires, heat, and greenspace contributing to ASD remain unclear, and the biological pathways linking these exposures to ASD are not fully understood. This project aims to address these critical gaps by integrating advanced metabolomics with novel methods for assessing multiple pollutant exposures. This K99/R00 award will provide Dr. O’Sharkey with the opportunity to enhance his expertise in environmental epidemiology and air pollution by incorporating additional training in metabolomics, neurodevelopmental epidemiology, and advanced analytical methods for multiple exposures and mixtures. This project will offer valuable insights into the biological pathways linking ASD and air pollution source exposure by leveraging a large metabolomics dataset, enabling a deeper understanding of the mechanisms underlying this complex relationship. Aim 1 will investigate the association between various AP sources, including traffic-related pollutants, wildfire smoke, and environmental factors like heat and greenspace, with ASD risk using advanced statistical methods to disentangle the combined, synergistic, and individual effects of these exposures. Aim 2 will examine how specific AP sources and components influence neonatal metabolomic profiles, using blood spots, and investigate the effects of related exposures like excess heat and green space access. Aim 3 will identify and characterize biological pathways linking AP exposure to ASD by comparing metabolomic profiles between ASD and non-ASD populations using neonatal blood spots. By integrating metabolomic data, the project aims to uncover specific biomarkers and disrupted metabolic pathways associated with AP exposure, providing novel insights into the mechanisms underlying ASD. Through the mentorship of Drs. Mike Jerrett and Beate Ritz, and the advisory committee of Dr. Rebecca Schmidt, Dr. Doug Walker, Dr. Zeyan Liew, and Dr. Kimberly Paul, Dr. O'Sharkey will accomplish these aims with the support of field experts, benefiting from dedicated one-on-one guidance, extensive collaboration, and regular lab meetings to foster ongoing progress and learning. Through targeted coursework, workshops, seminars, and the K99/R00 award's support for attending conferences, Dr. O’Sharkey will gain advanced skills in environmental and metabolomics epidemiology, ensuring a comprehensive and multidisciplinary training experience. The findings of this research could significantly inform public health policies aimed at reducing specific sources, components, or mixtures of AP exposure during pregnancy, contributing to environmental epidemiology by revealing previously unknown metabolic pathways affected by low-concentration pollutants.

NIH Research Projects · FY 2025 · 2025-09

The discovery of new pharmaceutical compounds relies on the ability to synthesize increasingly complex scaffolds with desirable biological and pharmacokinetic properties. To foster continued growth in this area, new transformations to access small organic molecules are required. Methods which form new C–N bonds are highly valuable, as over 50% of the top two hundred drug compounds contain at least one N-atom. Recently, the Doyle group has demonstrated that sulfonamides can be activated for hydroamination reactions using photoredox/phosphine catalysis through α-scission of an intermediate phosphoranyl radical. While phosphoranyl radicals have been used to enable a variety of transformations through β-scission, transformations proceeding through α-scission are underdeveloped despite providing an approach to form C–N or C–heteroatom bonds. To promote the development of new transformations relying on phosphoranyl radicals, a better understanding of the factors controlling α- versus β-scission is required. We propose leveraging data science to build a comprehensive, predictive model explaining phosphoranyl radical α- versus β-scission selectivity. This model will employ data collected from a model reaction to train predictive machine learning algorithms, overcoming limitations in our current mechanistic understanding of this process. In addition, we will leverage phosphine activation of N-nucleophiles for a novel alkene carboamination reaction based on a photoredox, phosphine, and nickel tandem catalysis. This unique approach relies on the ability of phosphine catalysis to form N-centered radicals and the established reactivity of nickel catalysis towards C-centered radicals to enable a desirable carboamination reaction without the requirement of pre-functionalized amines or stoichiometric reagents. In addition, this approach is amenable to carboamination with N-heterocycles. Overall, the proposed research is comprised of two aims: (1) develop predictive machine learning algorithms to determine factors influencing the selectivity of α- and β-scission of phosphoranyl radicals, and (2) develop a photoredox, phosphine, and nickel catalysis mediated carboamination reaction. These aims will be explored concomitantly with data collected in aim 1 being applicable to, but not necessary for, the completion of aim 2. Aim 1 will contribute significantly to our understanding of phosphoranyl radical reactivity, promoting the design and development of new transformations relying on this powerful mechanistic step. The transformation developed in aim 2 will address challenges in currently available carboamination reactions to generate desirable C–N and C–C bonds and will provide a framework for future reactions combining photoredox, phosphine and nickel catalysis. Overall, this work represents a significant advance in the field of phosphoranyl radical reactivity and is expected to have longstanding impacts on future applications of these approaches to pharmaceutically relevant transformations.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY/ABSTRACT Social dysfunction is a core feature of schizophrenia, and individuals with a schizophrenia diagnosis often lack strong social ties to friends and family. This type of social disconnection is detrimental to health and associated with reduced quality of life. However, current treatments have little to no effect on social functioning and connectedness in schizophrenia, and new treatment approaches are urgently needed. Yet, poor understanding of the neural mechanisms that underlie the formation and maintenance of social connections in schizophrenia has hindered the development of novel interventions. Recent social neuroscience research has identified inter- brain synchrony as an important neural mechanism that promotes the formation of social ties by facilitating successful social interactions in healthy samples. Converging evidence suggests that this mechanism may be disrupted in schizophrenia, yet it has never been investigated directly in a schizophrenia sample. This project will investigate inter-brain synchrony and its relevance to social connection in schizophrenia for the first time, using methods adapted from non-clinical research. In this study, participants who have a schizophrenia diagnosis and healthy control participants will each take part in structured social interactions with two different laboratory confederates while electroencephalographic (EEG) recordings are collected simultaneously from both the participant and the confederate. The interaction with one confederate will include a conversation structured to induce a feeling of interpersonal closeness (experimental condition), while the interaction with the other confederate will include a conversation structured to include only superficial small-talk (control condition). The amount of inter-brain synchrony between participants and confederates will be assessed based on EEG activity from just before and after each conversation, during a brief collaborative task. This study has two major goals. First, the project will investigate inter-brain synchrony as a neural mechanism of social dysfunction in schizophrenia by comparing synchrony between the schizophrenia and control samples as well as investigating relationships between levels of inter-brain synchrony and participants’ social connectedness. Second, the project will test the malleability of inter-brain synchrony measures by comparing synchrony between the two experimental conditions and the pre- vs. post-conversation measures. As an additional goal, the project will investigate relationships between inter-brain synchrony and interpersonal synchrony of behavior measured during the social interactions, and with clinical variables (e.g., symptoms). Achieving these goals has the potential to transform the understanding and treatment of social dysfunction and disconnectedness in schizophrenia by elucidating the role of a novel neural mechanism. This study could offer new treatment targets for interventions to improve social functioning, and it could provide a basis for the development of novel biomarkers to assess social functioning in schizophrenia and other mental health conditions.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Proteases, particularly matrix metalloproteinases (MMPs), are pivotal in the malignant progression of solid tumors, mediating invasion, migration, and metastasis via remodeling of extracellular matrix (ECM). Recent studies have shifted focus from the abundance of MMPs to their functional roles, especially in profiling proteolytic activities. However, detecting MMP activities within tumor tissue pose challenges, necessitating complicated procedures involving invasive acquisition of tumor tissues and labor-intensive purification of MMPs. This is especially more challenging for bone cancer tissues, as bone tissue biopsies typically undergo decalcification, which can severely damage the proteins in the tissue. Tumor extracellular vesicles (EVs) transport MMPs from tumor cells while preserving their functional activities, making them ideal surrogates for the parent tumor tissue for noninvasive profiling of MMP activities. Osteosarcoma (OS), the most common pediatric bone cancer is in pressing need of a quantitative liquid biopsy assay for assessing treatment responses for both localized OS and metastatic OS. Therefore, assessing OS EV MMP activities holds significant promise to serve as a liquid biopsy assay that can supplement radiographic imaging for assessment of treatment responses. Over the past decade, our UCLA team has pioneered click chemistry-mediated tumor EV enrichment technologies, i.e., Click Beads and Click Chips, enabling diverse downstream molecular analyses, such as mRNA profiling and protein quantification. Recently, we demonstrated the feasibility of coupling functional analysis, i.e., MMP activity assay using the enriched tumor EVs, offering a strong potential to deepen our understanding of MMPs’ pathological roles and to develop new cancer diagnostic solutions. The long-term goal of this TTNCI R01 proposal is to further refine, validate, and translate a streamlined OS EV MMP Activity Assay for monitoring treatment responses in OS patients who receive treatment interventions, e.g., a combination of neoadjuvant chemotherapy, surgery, and adjuvant chemotherapy. This two-step assay involves: i) click chemistry-mediated enrichment of subpopulations of OS EVs using EV Click MagBeads, in the presence of trans-cyclooctene-grafted antibodies targeting the respective OS EV surface markers, and ii) FRET peptide probes for assessing the activities of OS- associated MMPs in the enriched OS EVs. The innovation of this proposal lies in i) innovative use of nanotechnology-enabled OS EV MMP Activity Assay to address an unmet clinical need in noninvasive assessment of treatment responses in an aggressive pediatric tumor, and ii) a rigorously developed design of experiments (DOE) plan to identify optimal parameters for the assay. The successful development of the proposed OS EV MMP Activity Assay is expected to achieve translational readiness for eventual GLP manufacturing and a Phase-2 biomarker study.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY While conventionally known as contrast agents for diagnostic ultrasound, gas-encapsulated microbubbles can also be functionalized with targeting antibodies and therapeutic payloads. This unique delivery mechanism allows for improved therapeutic index in difficult-to-reach locations, minimizing the risk for off-target effects. However, the potential of microbubble theranostic agents has not yet been fully realized because of current limitations in conjugating antibodies and therapeutic agents to the microbubble itself. These limitations include difficulties in conjugating the antibodies in the right alignment on the microbubble while preserving microbubble integrity. It is also unclear what the pharmacokinetic characteristics of the theranostic microbubbles will be in a clinically-relevant large animal model. We plan to overcome these limitations using a novel site-specific labeling technique known as Light Activated Site-Specific Conjugation (LASIC) technology. Leveraging LASIC to attach both targeting antibodies and therapeutic payloads will streamline the theranostic microbubble manufacturing process, facilitating its translation into the clinical population. With successful LASIC-optimization of microbubbles, the biodistribution of these microbubbles will be further improved via image-guided catheter delivery. This project will be conducted via 3 aims: 1) Creating targeted microbubbles using LASIC technology: We will test the feasibility of LASIC on commercially available avidin-coated, and then the less immunogenic azido-lipid-based microbubbles. Efficacy of microbubble-cell binding will be evaluated using in vitro assays and ultrasound phantoms. We will then 2) Generate theranostic microbubbles using LASIC to attach therapeutic payloads to the microbubbles. In this aim we will also augment microbubble payload release via non-invasive sonoporation, using in vitro techniques to optimize drug delivery. Lastly, we will 3) validate the safety and pharmacokinetic profile of the LASIC-optimized microbubbles large animal tumor model, also known as the Oncopig. The microbubbles will be delivered via a clinically relevant image-guided catheter, allowing for maximum therapeutic index in the target tumor. The results of this project will have significant implications for the translatability of microbubble platforms for theranostic purposes. By minimizing disruption of the phospholipid shell, LASIC can potentially revolutionize the way microbubbles are currently conjugated. The potential implications of this research extend well beyond the immediate impact of microbubble conjugation chemistry, paving the way for future advancement in theranostic agents, image-guided interventions, and large animal validation modeling.