Stanford University

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY This proposal outlines a five-year career development program for Chris McGinnis, Ph.D. to prepare him for an independent research career in cancer immunology to study tumor-immune interactions during metastasis. The candidate will conduct his postdoctoral training at Stanford University, which provides an outstanding environment to complete the proposed research. Dr. McGinnis’ mentors and advisors, including Drs. Satpathy, Kuo, Engleman, Reticker-Flynn, and Engreitz, have diverse technical expertise relevant to all aspects of the proposal and track records of guiding trainees to independence. Further, the candidate will utilize world-class resources available through the Stanford Office of Postdoctoral Affairs to acquire career development skills. Additionally, the research infrastructure within his mentors’ labs will enable him to efficiently perform the scientific aims, receive training in areas encompassed by this proposal, and transition to independence. The goal of this work is to demonstrate the utility of Dr. McGinnis’ innovative research program – namely, coupling longitudinal single-cell RNA-sequencing (scRNA-seq) and chemical compound high-throughput screening (HTS) to improve our understanding of pro-metastatic immune remodeling mechanisms and discover the first-generation of anti-metastatic immunotherapies. The role of tumor-mediated immune remodeling during metastasis is well-established, positioning immunotherapeutic approaches as an attractive paradigm for limiting metastasis. However, our understanding of these mechanisms remains incomplete, and existing HTS platforms are ill-suited to identify drugs which effectively operate in the metastatic nice. In Aim 1 (K99), Dr. McGinnis will demonstrate how longitudinal scRNA-seq analysis of the metastatic niche can uncover novel mechanisms of pro-metastatic immune remodeling by formally assessing whether IGF1 signaling between lung neutrophils and tissue-resident macrophages regulates early organization of the pre- metastatic niche. In Aim 2 (K99), the candidate will benchmark and optimize an ex vivo lung tissue culture HTS platform which will be used to analyze immune perturbation responses in the metastatic niche using scRNA-seq. After transitioning to a faculty position for Aim 3 (R00), Dr. McGinnis will focus his efforts on performing the first anti-metastatic immunotherapy chemical compound screen to identify drugs that effectively inhibit myeloid TLR- NFκB inflammation in the lung metastatic niche. Together, the pursuit of this research will provide critical insights into how tumors use the immune system to spread and identify anti-metastatic immunotherapy candidates. All protocols, data, analytical frameworks, and software tools that are produced during the duration of this research will be freely distributed. Moreover, the proposed project will serve as an effective training program for Dr. McGinnis to launch his independent career as a tenure-track investigator.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY/ ABSTRACT Lung transplantation and hematopoietic cell transplantation (HCT) are life-saving treatments for a variety of diseases, but survival after transplant can be limited by a transforming growth factor (TGF)-β-mediated pulmonary fibrosis. Transplant-related pulmonary fibrosis (TPF) is a rare condition that affects the distal airways, known as bronchiolitis obliterans syndrome (BOS). In lung transplants, fibrosis can also affect the lung parenchyma and pleura, collectively known as chronic lung allograft dysfunction (CLAD), the major obstacle to long-term survival after lung transplant. Yet, there are no available treatments for TPF. We recently completed a phase I trial that examined pirfenidone, a TGF-β inhibitor, FDA-approved for idiopathic pulmonary fibrosis (IPF), for use in persons with BOS after HCT (NCT03315741). Treatment with pirfenidone was associated with an improvement in pulmonary function tests, as well as a decrease in liver and skin markers of chronic Graft-Versus- Host-Disease (GVHD), suggesting a role for antifibrotics in alloimmune-mediated fibrosis. The transition of pirfenidone to the generic drug market has prevented additional drug sponsorship necessary for a trial seeking FDA-approval for TPF. Hence, we are focused on developing new TPF drugs that target the TGF-β pathway. In this proposal, we outline the development of a small molecule, called Shc301, that inhibits Src Homology and Collagen (Shc) signaling involved in a non-canonical TGF-β pathway. In preliminary studies Shc301 treatment: (i) decreased markers of fibrosis in TGF-β stimulated human precision cut lung slices (hPCLS) obtained from patients with IPF and CLAD; (ii) in a mouse orthotopic tracheal transplant (OTT) model of TPF, treatment with Shc301 decreased fibrosis, when used in both a preventative (after transplant) and a treatment-based strategy (after 21 days of acute graft rejection), and (iii) Shc301 was more effective than pirfenidone in mitigating TPF in both models. In the OTT model, fibrosis in the Shc treated allotransplants was equivalent to that seen in the non- rejecting syngeneic transplants. This proposal will test the central hypothesis that Shc301 can mitigate fibrillar collagen accumulation and crosslinking in preclinical models of TPF. Specific Aim 1 will assess the efficacy of increasing doses of Shc301 in hPCLS derived from patients with CLAD and HCT-BOS at the time of lung transplantation. Specific Aim 2 will evaluate the in vivo efficacy, pharmacodynamics, and pharmacokinetics of Shc301 in the mouse OTT model. In both aims the primary outcome measure for efficacy will be collagen levels, as quantified by the hydroxyproline assay. Secondary correlative measures will include Second Harmonic Generation microscopy to quantify tissue stiffness (i.e., fibrillar collagen), and the expression of pro-collagen precursors ⍺-smooth muscle actin and collagen type I, alpha 1. Pharmacokinetic analysis will use liquid chromatography-mass spectrometry to measure plasma and lung tissue concentrations over time in the murine airway transplant recipients. Successful completion of the proposed studies would represent a first step toward a paradigm shift in TPF treatment, providing rigorous evidence supporting the further development of Shc301.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY: Neurodevelopmental disorders impact up to 24% of children. The pathophysiology of most neurodevelopmental disorders is poorly understood preventing therapeutics; however, calcium dysregulation and interneuron dysfunction are implicated in many disorders. CACNA1C encodes a CaV1.2 L- type calcium channel subunit. Variants in CACNA1C cause a spectrum of neuropsychiatric symptoms. A CACNA1C gain of channel function variant impairs interneuron migration and cortical development in human induced pluripotent stem cell (hiPSC) derived neural organoid and assembloid models. We do not know the neuronal pathophysiology of CACNA1C loss of function or channel-neutral variants. Investigating the interneuron phenotypes of clinically severe CACNA1C variants will elucidate downstream pathways regulating interneuron pathology that contribute to a cortical network phenotype, potentially indicating treatments. The applicant, Dr. Rebecca Levy, proposes to employ gene editing to develop novel hiPSC lines and neural assembloids, then leverage calcium indicators, live imaging, and RNA sequencing analysis to determine how CACNA1C variants impact mechanisms directing interneuron migration. In Aim 1, Dr. Levy will learn CRISPR/Cas9 gene editing to generate 3 novel hiPS cell lines of CACNA1C including variants that cause loss of function and no change in calcium flux in non-neuronal cells. She will hone her skills in neural organoid culture to derive subpallial organoids and quantitate calcium activity with Fura-2 and interneuron-specific GCaMP calcium indicators, as well as CaV1.2 channel agonists and antagonists to rescue phenotypes. In Aim 2, Dr. Levy will develop cortical-subpallial assembloids and use live imaging to measure interneuron migration saltation length and frequency, then how cortical network activity is affected within assembloids. In Aim 3, Dr. Levy will confirm mechanisms via Western blot, pharmacologic rescue, and single cell RNA sequencing analysis. This will also generate new hypotheses about pathways affected in both gain and loss of function variants for future grants. Dr. Levy is a neurogeneticist with experience in mouse and neural organoid models. Her career development plan addresses training gaps in gene editing, neural assembloid culture, advanced imaging techniques, expression analysis, and scientific communication. Her mentor, Dr. Sergiu Pasca, is a leader in neural organoid culture techniques. Her advisory mentors are Dr. Porteus (gene editing), Dr. Hall (Cacna1c rodent models), Dr. Huguenard (systems neuroscience), and Dr. Bernstein (rare disease geneticist). Stanford provides a rich environment including core facilities, seminars, courses, and collaborations. With this dedicated mentorship team, outstanding institutional environment, and thoughtful training and career development plans, Dr. Levy has the resources and support to launch her independent physician-scientist career. This proposal will discover interneuron pathophysiology caused by CACNA1C variants with variable impact on channel function, identifying core mechanisms that could become therapeutic targets.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY The need for more robust evidence to predict risk and prevent suicide among Black youth is critical in light of the rapidly rising rates of suicidal behaviors in this population. As the third leading cause of death in Black youth aged 12-18, the timely identification of suicide risk is a critical public health priority. Yet, there is a fundamental gap in suicide research focused on Black populations in these transitional developmental stages due to limited investigations of everyday stressors as a unique antecedent to suicide and its potentially synergistic effect on known risk factors. Digital phenotyping platforms offers an innovative opportunity to collect real-time data associated with everyday stressors by integrating active (e.g. ecological momentary assessment-EMA) and passive (e.g. GPS, accelerometer, etc.) data using smartphones. Refined digital phenotyping platforms may provide more granular insights towards Black youth’s proximal suicide risk by assessing time-varying factors as they naturally occur. Specifically, the candidate plans to address the following specific aims: (1) Modify an existing digital phenotyping protocol for appropriate use among Black youth; (2) Conduct a pilot digital phenotyping study among a re-contacted and newly recruited psychiatric sample of Black youth to determine feasibility and acceptability of real-time assessments of suicidology in the study population. The proposed K01 project enhances the candidate's prior research in mental health disparities and health services research among Black adolescents and will uniquely position the candidate to enhance suicide risk detection methodologies using real-time and engaging digital strategies. To achieve these career objectives, the applicant will work with a highly skilled mentorship team to build four areas of expertise relevant to this research agenda: (1) intensive longitudinal design and analysis, (2) suicide risk identification, (3) engaged and responsive recruitment and retention strategies, and (4) translation of research for suitability in youth populations. The K01 award will increase the applicants’ capabilities as an independent researcher to develop and test fully powered real-time smartphone-based approaches among youth.

NSF Awards · FY 2025 · 2025-07

This I-Corps project focuses on the development of an artificial intelligence scientist system that automates biomedical research and development workflows. Current biomedical research processes are significantly hindered by labor-intensive manual operations, creating severe bottlenecks in experimental design and data analysis. The technology addresses these limitations by actively collaborating with scientists throughout the research process, from hypothesis generation through experimental execution to results interpretation. By integrating advanced language models with domain-specific biological knowledge, the system serves as an intelligent research partner across the entire scientific pipeline. The technology automates routine laboratory procedures, enhances experimental planning, and performs comprehensive data analysis, thereby reducing time spent on repetitive tasks while accelerating the generation of novel biomedical insights. This approach maintains rigorous scientific standards while dramatically improving research efficiency. The inefficiency in current biomedical research affects thousands of laboratories globally, resulting in wasted resources and significantly delayed scientific breakthroughs. The widespread adoption of this technology would accelerate development timelines for treatments targeting cancer, rare genetic diseases, and other critical health challenges, ultimately advancing public health outcomes and accelerating scientific progress. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a multi-agent architecture that enables complex task decomposition and sophisticated experimental planning through advanced computational modeling. The system implements chain-of-thought reasoning, reinforcement learning techniques, and retrieval-augmented generation to create intelligent agents that work alongside scientists in executing research workflows. By integrating natural language processing with specialized knowledge extracted from the peer-reviewed literature, the system generates scientifically valid experimental designs, offers predictive insights, and analyzes results with precision exceeding traditional approaches. The technology represents a fundamental advancement in computational biology by merging machine learning capabilities with deep biological domain expertise, ensuring all decisions align with established scientific principles while dramatically reducing time requirements for routine research activities. Scientists using this system benefit from substantially increased experimental throughput, enhanced reproducibility of results, and the ability to explore more hypotheses in less time, potentially transforming discovery rates across the biomedical sciences and accelerating innovations in human health. 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 · 2025-07

This award is made in response to Dear Colleague Letter 24-130, as part of the ECosystem for Leading Innovation in Plasma Science and Engineering (ECLIPSE) interdisciplinary program. The award supports an effort to advance the predictive modeling capabilities of low-temperature plasmas (LTPs) for improved fabrication of microelectronics systems. LTPs are weakly ionized gases that play an important role in many industrial applications, including materials processing, spacecraft propulsion, and hypersonic flights. Predictive models of LTPs can help to advance the understanding of how to control physical and chemical processes within a low temperature plasma, and how to design the next-generation industrial systems. The main objective of this project is to develop theoretically accurate and computationally efficient models that can capture the critical physical and chemical processes in LTPs. The research will be conducted in conjunction with Applied Materials, which will help enhance industry workforce development in the United States. Fluid equations are typically derived by taking the moments of the first-principles gas kinetic equations, such as the Boltzmann equation. The main problem of the state-of-the-art fluid models for LTPs, including drift-diffusion models and local approximation, is the inability to account for nonlocal effects of electrons. While kinetic models, such as particle-in-cell Monte Carlo collision simulations, capture such nonlocal phenomena, the computational cost is often much more expensive than fluid models, limiting their utility as design tools for industrial applications. The project aims to bridge the gap between fluid and kinetic models by developing closure models based on high-order moment models (HOMMs) that capture nonlocal and kinetic effects of LTPs. Specifically, the research objectives are to: (i) revisit and develop HOMMs, solving for the moments up to the fourth moment, including mass, momentum, pressure, heat flux, and kurtosis; (ii) investigate stochastic heating and nonlocal heat flux effects in RF-driven plasmas; and (iii) apply the HOMMs to study microwave plasmas used for microelectronics fabrication. This award is jointly supported by the Division of Physics and the Office of Advanced Cyberinfrastructure. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-07

Prescribing errors are common in inpatient pediatrics, resulting in preventable harm to up to 6000 US children per year. These errors occur within 3 overlapping contexts: individuals susceptible to human error, interprofessional teams susceptible to ineffective communication, and systems with inadequate safeguards. However, our understanding of the incidence of prescribing errors and the contribution of these contextual factors is limited because neither prescribing errors nor their contextual factors have been reliably measured or assessed at scale. Our central hypothesis is that adding novel context measures to traditional error prediction measures can enable us to predict prescribing errors with high accuracy. Preliminary work by our group and others have built algorithmic error identification tools using electronic health record (EHR) metadata and identified measures for each of these contexts that are predictive of prescribing errors. Building on this work, this proposal aims to validate the algorithmic approach to detecting pediatric prescribing errors at scale, validate the use of EHR metadata to identify individual, team, and system contextual factors which predict errors, and develop a machine learning model that can predict errors. In the first aim, we will build upon existing wrong-patient order identification tools to validate a broad algorithmic approach to detect pediatric prescribing errors, comparing algorithmic detection of prescribing errors to gold-standard blinded expert prescription review. In the second aim, we will validate the use of EHR metadata to measure team composition and interactions through a comparative focused ethnographic research approach incorporating in-depth observations and interview findings in relation to EHR metadata. Finally, in the third aim we will evaluate individual, team, and system context factors in relation to prescribing errors at three large children’s hospitals, using hierarchical logistic regression to quantify associations and testing the ability of machine learning prediction models to predict prescribing errors using EHR metadata with high accuracy. Through these aims, we expect to identify novel approaches to measuring the inpatient pediatric context and to develop a machine learning model incorporating relevant contextual factors to predict prescribing errors. These findings will be readily generalizable, as they are based on ubiquitously-collected data, enabling a multicenter trial of a transformational approach to clinical decision support, in which context factors inform real- time feedback on high risk prescriptions to prescribers, pharmacists, and nurses caring for the most vulnerable children.

NSF Awards · FY 2025 · 2025-07

This award funds research to develop a new methodology to distinguish between research findings that can be generalized across populations, places, and time, and those that cannot be generalized. This research addresses a fundamental challenge in empirical research: determining which experimental or observational findings are generalizable across different environments and populations. Existing methods do this by using restrictive assumptions, potentially leading to false generalization. By introducing a new methodology to detect generalizability, this research helps identify features of the environment, population characteristics, and treatment conditions that systematically contribute to generalizable results and those that exhibit context-specific or unpredictable results, and therefore not generalizable. The research results improve the reliability of evidence-based decision recommendations and the quality of decision design. By offering a rigorous approach to identify generalizability, this research makes significant contributions to economics science and beneficially informs decision makers and practitioners. The results of this research aid improved decision making, speed up economic growth, and hence improve living standards. This award funds a research agenda that develops a new methodology to distinguish between research results that are generalizable and those that are not. Methodologically, the research advances statistical meta-analysis by developing estimators and classification tools that distinguish between predictable (generalizable) and unpredictable (environment-specific) treatment effects. Unlike standard approaches, this framework allows researchers to pinpoint critical environmental or demographic factors driving effects heterogeneity. The resulting methodology can be integrated into various fields---including economics, public health, and education---enabling more nuanced insights into whether, when, and why certain policies or interventions are particularly effective. Empirically, the project applies these techniques to varied datasets in economics. 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 · 2025-07

PROJECT ABSTRACT The cornea is the clear window into the eye through which light must pass in order for the eye to see. It can be damaged by infections or injury. When the cornea and ocular surface is severely injured there is currently no effective treatment for vision preservation. In addition to disease, there are thousands of patients who suffer such injuries in work related accidents. Due to the industrial nature of many ocular chemical and thermal burns, corneal injury disproportionately affects those in the prime of their life. Building on the most recent developments in small image and wireless transmission technology we have designed an intraocular projection system that can be implanted into the eye in these patients. The device will wirelessly receive video data and power from a camera and processor positioned upon the frame of a pair of glasses. A small optical apparatus will focus the image on the retina. This in effect will place a microscopic office projector inside the eye so that it will be able to see even with a scarred cornea or even if the eyes are closed. Here we propose to test this device in rabbits. Our first aim is to build and implant 25 dummy lenticule implants of different weights into rabbits and observe them for 6 months. This is to identify how heavy intraocular implants can be before becoming unstable, a current unknown. Our second aim is to build and implant 25 semiactive lenticule implants into rabbits and observe them for 6 months at different power use levels. This is to identify the power safety limit of these implants, another unknown. In the penultimate aim, we will implant 11 fully functioning implants into rabbits. We will observe these rabbits for 6 months and measure their vision using brain signals to examine the long-term safety and efficacy. This project will provide important information and technology for development of a new class of visual restoration devices. It will pave the way for the eventual use of such a device to restore vision in humans.

NIH Research Projects · FY 2025 · 2025-07

Project Summary Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults with an average survival of 15-18 months. The heterogeneous nature of GBM is complicated by the presence of diverse cancer and non-cancer cells within the tumor. The intercellular crosstalk, including protein translocation, in GBM promotes chemoresistance, invasion, and angiogenesis and leads to a dismal prognosis. Despite extensive cancer biology research, a detailed molecular picture of protein trafficking between GBM and other non-cancer cell types remains largely unclear. This proposal aims to develop a new molecular method for mapping translocated proteins between GBM cells and other brain cell populations in an in vivo setting. This work is built upon an in-vivo compatible proximity labeling enzyme LipoID, which I developed early in my postdoctoral career. LipoID could employ unnatural substrates containing a click reaction handle to label the living glioblastoma proteome. In order to distinguish resident and translocated proteins within the destination non-cancer cells, this proposal aims to develop a new esterase-activated bioorthogonal reaction that allows for stepwise proteome labeling in two different cell types (Aim1). This method will enable the precise recording of protein translocation with cell-specificities in both GBM cells and non-cancer cells. In Aim2, systematic profiling of intercellular protein trafficking within the GBM microenvironment will be performed in both in vitro co-culture system (Aim2.1), and in vivo syngeneic models (Aim2.1). Quantitative protein mass spectrometry will be used identify significant protein translocation events between GBM and other brain cell types (neurons, astrocytes, microglia, and endothelial cells). To functionally validate the importance of the top proteomic targets, Aim2.3 will focus on creating (conditionally) knock out GBM cells for syngeneic GBM models generation. Tumor growth and mice survival will be measured and compared with the effect of Wildtype GBM cells. This study could open exciting avenues of research in understanding gliomagenesis, and potentially unveil new targets for GBM therapeutic development. The esterase-activated click reaction may find broader usage in biomedical research by providing spatial and temporal resolution for bioconjugation. My mentor and collaborators possess extensive expertise in molecular tool development, brain cancer biology, quantitative proteomics, bioorthogonal chemistry, and glioblastoma mouse models, thereby equipping me with the necessary training to execute the proposed research. Additionally, they will offer mentorship to acquire all requisite professional skills and preliminary data for a successful transition to my independent cancer research lab. There, I aim to dedicate my career to unraveling cancer-causing mechanisms and developing innovative cancer therapies.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY Colorectal cancer is the fourth deadliest cancer in the US and the second deadliest in persons under the age of 50, with a five-year survival rate less than 66%. Immune checkpoint inhibitors have become the standard of care for patients with microsatellite-instability high and mismatch- repair-deficient colorectal cancers, but these represent only ~7% of patients. Within the other 93% of patients, some respond to immune checkpoint inhibitors, but without predictive biomarkers to identify those who will benefit, the survival benefit is not enough to justify clinical use. Interacting cellular communities in the tumor microenvironment comprised of cancer, immune, and other cells can promote or restrain the antitumor immune response. These communities, termed ecotypes, can be described by the transcriptional states of their constituent cells. Work in other cancers has identified ecotypes that maintain functional or hypofunctional T cell states and predict response to immunotherapy. The central goal of this proposal is to identify spatially organized ecotypes in colorectal cancers that govern antitumor T cell activity and predict patients’ response to immunotherapy across all forms of colorectal cancer. This project will use a transcriptomic atlas of more than 200 colorectal cancer patients’ tumors curated from published data, including more than 170 pre-treatment samples from patients treated with immune checkpoint inhibition. A novel deep learning algorithm designed for the proposal will be used to identify spatial ecotypes in the colorectal cancer microenvironment from transcriptomics data. In the long term, robust predictive markers of response to immunotherapy would expand the population of patients eligible for immunotherapy by identifying the microsatellite-stable cancer patients most likely to benefit. Defining these predictive markers in terms of spatially organized multicellular communities will identify the cell-cell interactions that promote or restrain antitumor immunity, which could ultimately identify new drug targets to augment or improve upon existing immunotherapies.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY The incidence of renal cell cancer (RCC) has been steadily increasing over the past three decades in the U.S. and it remains the most lethal urological malignancy due to a lack of screening for early detection. While excess body adiposity at the individual level is a recognized modifiable risk factor for RCC, the influences of obesogenic factors beyond individual level, particularly the molecular mechanisms and neighborhood environments, remain unexplored. In addition, the impact of body adiposity on RCC survival is debated, with evidence suggesting lower mortality among obese patients (i.e., obesity paradox). The scientific objectives of this proposal are to 1) investigate how adiposity-related factors at the molecular, individual, and neighborhood levels interplay to influence the RCC continuum—incidence, tumor aggressiveness, and survival—to reveal underlying mechanisms and uncover novel prevention avenues; 2) apply advanced causal inference methods to elucidate the true impact of body adiposity on RCC survival, the obesity paradox phenomenon. Aim 1 of this proposal will use causal mediation analysis with the UK Biobank (UKB) database to identify biological pathways through which excess body adiposity increases RCC incidence. Aim 2 will elucidate the true impact of body adiposity on RCC survival, examining life-course pre-diagnosis anthropometrics in the NIH-AARP Diet and Health Study (AARP) as well as pre-diagnosis adiposity-related biological pathways using the UKB. State-of-the-art causal inference methods will be implemented to ameliorate confounding, selection bias, and reverse causation that were inadequately addressed in previous studies. In addition, Aim 2 will explore how pre-diagnosis adiposity influences RCC aggressiveness, to test a potential biological hypothesis for the obesity paradox. Aim 3 will investigate how neighborhood socioeconomic characteristics and built environments that can promote weight gain (i.e., obesogenic environment) affect RCC incidence, aggressiveness, and survival. This aim will implement advanced multilevel methods using data from several large prospective cohort studies, including the AARP study, Nurses’ Health Study (1 and 2), and Health Professional Follow-up Study. The proposed research seeks to unravel the multilevel impacts of adiposity- related factors on the entire RCC continuum, providing insights for the underlying biological mechanisms and multilevel prevention efforts to reduce RCC incidence and improve outcomes. This research proposal is complemented by a career development plan that builds upon applicant’s background in medicine, environmental health, and cancer epidemiology. Specifically, this career development plan outlines new training in three areas: 1) molecular epidemiology in kidney cancer; 2) advanced causal inference methods; 3) multilevel approach for cancer prevention. The combined research and training plan will prepare the applicant for a successful independent research career identifying, evaluating, and implementing multilevel interventions to prevent cancer and promote overall health.

NIH Research Projects · FY 2025 · 2025-07

PROJECT SUMMARY The immune response of solid organ transplant rejection is a complex process that can lead to the loss of the transplanted organ. Transplant rejection is broadly characterized as antibody-mediated rejection (ABMR) and T cell-mediated rejection (TCMR) with evidence showing that ABMR confers a higher risk for graft loss. A significant number of renal biopsies exhibit a mixed TCMR/ABMR phenotype, suggesting there is more disease overlap than previously known. Unfortunately, irreversible graft damage often predates the diagnosis of ABMR, and treatment options are costly with debatable efficacy, underscoring the need for improved earlier diagnostics and prognostics. Evidence supports that local renal immune microenvironment dysregulation may play a significant role in renal rejection pathobiology. This proposal aims to address the limitations of current histopathological assessments in predicting post-transplant outcomes by providing a granular atlas of the immune cell interactions and spatial distributions in the renal microenvironment. The central hypothesis of this proposal posits that higher abundance, interactions, and activity of cytotoxic immune cell subsets correlate with more severe ABMR disease and worse treatment responses. To test this hypothesis, we will utilize advanced imaging mass cytometry to comprehensively profile the immune cells and their functional states within kidney tissues. Experiments proposed in Aim 1 will establish an immune atlas that differentiates between ABMR and TCMR by examining immune cell phenotypes, functions, and spatial patterns in renal biopsies. Aim 2 will integrate this immune data with clinical variables to develop predictive models for treatment response, using a novel machine learning algorithm specifically designed for high-dimensional data analysis. This work will be completed at Stanford University School of Medicine in Dr. Brice Gaudilliere’s laboratory (primary sponsor) with guidance from Dr. Minnie Sarwal (co-sponsor, UCSF). The proposed rigorous training plan harnesses cutting-edge high-dimensional single-cell spatial immune profiling with sparse machine learning methods. The training, approach, and results generated will offer a unique framework for future research on cytotoxic mechanisms of renal transplant rejection, aiding my career development and enabling me to develop the expertise in transplant immunology and computational biology necessary for becoming an independent academic translational physician-scientist. Importantly, these findings will enhance our understanding of the immune dynamics in kidney transplantation and pave the way for innovative diagnostic tools and therapeutic interventions, ultimately improving patient outcomes and graft survival rates. This research holds significant promise for advancing personalized medicine in the field of organ transplantation.

NSF Awards · FY 2025 · 2025-07

This project will make advances on fundamental problems in Ramsey theory and extremal combinatorics, which study conditions guaranteeing the existence of patterns in discrete structures, through the further development of techniques from a variety of areas of mathematics, including from combinatorics, probability, analysis, and algebraic geometry. These methods and problems are not only of intrinsic interest but relate to important applications in computer science, including the development of faster algorithms for problems of real-world utility like matrix multiplication and understanding the properties of large networks. Students will be mentored as part of this research project. The first area in this project concerns Ramsey numbers of hypergraphs, including better understanding the role of the number of colors and how the structure of hypergraphs impacts the off-diagonal growth rate. The PI and his collaborators have previously made significant progress on these directions. The PI also plans to continue studying the Erdős-Hajnal conjecture and variants, which roughly shows that graphs with a forbidden substructure are well-structured (have large independent sets or cliques). Finally, the PI plans to continue collaborative work on Alon's conjecture on the existence of Ramsey Cayley graphs for all finite groups and related longstanding problems in additive combinatorics, information theory and random graph theory. 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 · 2025-07

Project summary The formation of memories is central to the human condition. It enables learning and the accumulation of knowledge over the lifespan, and it allows using the past in order to inform the future. While the mechanisms underlying the neural basis of memory have been extensively studied in the brain, whether memory exists in the peripheral nervous system, and in particular the enteric nervous system (ENS), remains unknown, representing a fundamental gap in our knowledge of peripheral neuroscience and gastrointestinal physiology. The goal of this study is to address this fundamental question. Our proposal is based on exciting preliminary evidence for the existence of memory in the ENS. We have observed the formation of enteric neural engrams in response to gastrointestinal stimuli and have characterized the molecular, temporal, and functional characteristics of these engrams. We now aim to build the tools required for studying the formation and meaning of ENS memories. We will then use these tools in combination with microbial, xenobiotic, and dietary perturbations in animal models, in order to transform our understanding of numerous functions and diseases of the gastrointestinal tract through the lens of enteric neural engrams. This project will impact the study of gastrointestinal and metabolic diseases in three major ways: First, it will uncover a fundamentally new aspect of ENS biology. Memory formation in the brain has been studied for decades, and its proper function is critical for organismal survival. Understanding the memory capacity of the ENS would enable us to add several new layers to the canonical functions of enteric neurons. Second, our study will provide new insights into the nature of memory. Memory formation is generally studied in response to sensory information (neuroscience) or antigen exposure (immunology). Our investigations will extend this concept, by highlighting biotic and abiotic gastrointestinal triggers of memory formation. Third, our study may provide the conceptual framework for an entirely new approach to treating diseases associated with the gastrointestinal tract – one that focuses on information stored in enteric neurons.

NSF Awards · FY 2025 · 2025-07

The first hint of an interaction between the mathematical fields of topology and analysis is the fact that every function on the circle must have both a minimum and a maximum, unlike, for example, functions on the line which need not have either. Over a century ago, Marston Morse discovered a vast generalization of this basic fact, which has been a cornerstone of subsequent development in topology. This project aims to develop a framework that makes it possible to take Morse's insight as a starting point for formulating a new framework in which to study algebraic structures. The broader impacts of this proposal include the training of future leaders of the field, as well as developing resources for understanding the relevance of topological notions beyond pure mathematics. The key technical notion in the proposal is that of a flow category. The PI plans to formulate algebraic structures geometrically at the level of the underlying flow categories in order to extend our ability to develop applications. In this way, the PI expects to make substantial advances in Floer homotopy theory and its applications both to symplectic topology, and to low-dimensional topology, via the study of Heegaard-Floer homology. 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 · 2025-07

PROJECT SUMMARY Pelvic organ prolapse (POP) is a debilitating condition characterized by the downward movement of the vaginal and/or the uterus and bladder through the vaginal opening. POP incidence is 40% in women between the ages of 50-79 years.1,2 Despite this high incidence, its underlying pathophysiology is still not entirely understood.3 Surgery is the main treatment option, but up to 29% of treated patients experience recurrence of vaginal prolapse.1,4 The anterior vaginal wall is not only the most likely site of POP, but also the most likely location for prolapse recurrence after prolapse surgery.5-7 Recurrent vaginal prolapse is treated with more surgery that has even higher rates of recurrence. There is a clear need for preventative and non-surgical strategies for women who are at risk of undergoing repetitive surgeries for recurrent vaginal prolapse. However, our ability to develop effective, novel treatments is hindered by the lack of understanding of the altered tissue micro-environment and cellular function in the human POP vagina after prolapse surgery. This proposal addresses this knowledge gap. The primary components of the vagina are fibroblasts, smooth muscle cells (SMCs), and connective tissue. The reciprocal interplay between these components within the vaginal microenvironment is integral to vaginal function.8 Histological and biochemical alterations in the vagina of women with POP have been widely documented showing decreased SMCs, altered contractile function,9,10 and connective tissue deficiencies.11,12 The central hypothesis of this project is that POP surgery induces changes in the connective tissue of the POP vagina in conjunction with changes in fibroblast function and SMC loss. This leads to deficient vaginal properties that render the vagina susceptible to recurrent prolapse. This hypothesis will be tested by pursing two specific aims: 1) Examine the effect of POP surgery on the human vagina; and 2) Investigate altered vaginal fibroblast function in recurrent vaginal prolapse after POP surgery. In the first aim, vaginal wall tissues obtained from postmenopausal women undergoing primary POP surgery (controls) will be compared to those of age-matched women undergoing repeat POP surgery for recurrent vaginal prolapse (cases) through tissue biomechanical testing, histology, and gene/protein assays. For the second aim, in vitro studies of case and control fibroblasts will be conducted to examine cellular gene/protein expression, the proteins secreted by fibroblasts during culture, and fibroblast function in response to TGF-1 (a cytokine involved in wound healing). The proposed research is innovative in its establishment of foundational knowledge on the microenvironment and cellular pathways and functions in the recurrent prolapsed vagina after primary POP surgery. The proposed research is significant because unless we have data on this new vaginal landscape, we cannot generate hypotheses to address the high failure rates of repeat POP surgeries. The results of this research will set the stage for development of effective treatments to address recurrent vaginal prolapse.

NIH Research Projects · FY 2025 · 2025-06

Chemoprevention is an important, but challenging field of study due to the lack of pre-cancer model systems for research studies. Familial adenomatous polyposis (FAP), a hereditary condition characterized by polyp formation and a high risk of cancer development, has been the focus of tens of chemoprevention clinical trials all investigating the same target, cyclooxygenase-2 (COX-2). However, due to inconsistent efficacy across studies and significant off-target effects, no agent is currently recommended for FAP chemoprevention. Furthermore, there is limited research on why these agents have failed, leaving a critical gap in understanding that needs to be addressed in order to guide the development of more effective therapeutic strategies. This proposal aims to better understand how signaling and metabolic pathways are altered in premalignant polyps in effort to design more targeted and effective treatments for FAP patients. To facilitate this goal, I have generated a biobank of normal and premalignant organoid samples derived from extensive multi-sampling of individual patients, capturing both inter-polyp and inter-patient heterogeneity. From this, I discovered that polyp organoids derived from the same patient display variable growth rates and differing responses to two COX-2 inhibitors, which could not be rescued by the downstream primary effector PGE2. Moreover, I found polyp organoids exhibit altered metabolism beyond COX-2, which I hypothesize is indicative of a broader metabolic reprogramming that can be explored for future chemoprevention strategies. With the advisory of a team of expert scientists in single cell analyses, proteomics, metabolomics, and CRISPR screens, techniques I will add to my skillset following completion of this proposal, I plan to uncover the mechanisms underlying polyp variable response to COX-2 inhibitors and explore novel metabolic strategies for chemoprevention. In Aim 1 I will investigate the mechanisms of resistance in polyp organoids that show poor responses to COX-2 inhibitors, with the goal of identifying combination therapies to lower toxicity. In Aim 2, I will investigate the mechanisms of COX-2 sensitivity by examining how COX-2 signaling and downstream eicosanoid molecules regulate the activation of signaling cascades that influence cell growth and cell state. Finally, Aim 3 will move beyond COX-2 signaling to identify new metabolic pathways driving cell growth in polyps, providing insight into pre-cancer metabolic rewiring and offering potential new therapeutic strategies. Ultimately, this research has the potential to uncover novel therapeutic strategies that can significantly improve the effectiveness of chemoprevention in FAP patients and pave the way for broader applications in the prevention of sporadic colorectal cancer.

NIH Research Projects · FY 2025 · 2025-06

Project Summary Exercise offers profound systemic benefits, leveraging multiple molecular pathways and mechanisms to enhance health. Exercise interventions have been shown to improve symptoms in cancer, cardiovascular diseases, and neurodegenerative disorders. Molecular mechanisms of exercise in disease prevention and reversal include anti-inflammatory effects and improved immune function, hormonal regulation, reduction of oxidative stress, and improved metabolic function. There is a growing body of literature on the individual pathways activated by exercise in specific disease cases, but a comprehensive investigation across the whole body is needed to identify pathways and molecules of interest to mediating the systemic benefits of exercise and complex interorgan communication. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) has generated an invaluable resource for identifying molecular changes in response to exercise. We will leverage their exercised rat profiling (naive, 1, 2, 4, 8 weeks post treadmill). This dataset is unique in being multi-modal and multi-organ allowing us to capture the broadest repertoire of exerkines and co-profile the expression of their receptors within the same organ and across organs. We will compare these findings to profiles from healthy (GTEx and HuBMAP) and diseased (including HTAN and psychENCODE) humans. We propose to define a comprehensive, multi-organ catalog of exerkines. Using public databases, we will pair the exerkines with their receptors when known, revealing the whole-body landscape of exercise-induced signaling. For each organ, we will cross-reference the exerkine and receptor molecules with the relevant diseased and healthy human dataset to uncover overlapping pathways between disease dysregulation and exercise modulation. We will bioinformatically simulate single-cell expression data to predict exerkine and receptor cell types, and further predict cell-cell interactions, validating candidate receptor ligand interactions by spatial omics assays in the healthy and cancerous human intestine. We will: Aim 1. Generate and identify exerkines and receptors for 17 organs in MoTrPAC. Aim 2. Decode exerkines and receptors inter- and intracellular signaling events and synergistic cell-cell communication Aim 3. Analyze response of exerkines to disease in humans

NIH Research Projects · FY 2025 · 2025-06

ABSTRACT Rare variants are abundant in the human population and contribute to a variety of genetic diseases. However, identifying impactful rare genetic variants from the multitude of inconsequential variants remains a challenge. Transcriptomics has emerged as a complementary assay to identify the effects of rare non-coding genetic variants as approaches using gene expression outliers have assisted prioritization of rare variants involved in rare diseases. We have previously developed a machine learning model, Watershed, to prioritize candidate causal rare variants by integrating outlier gene expression levels with relevant genomic annotations such as conservation and regulatory element annotations. This approach expands on variant effect prediction tools that are genome-only by further considering an individual’s transcriptome alongside their genome in the variant prioritization strategy. While previous approaches have focused on rare variant identification in regions proximal to the expression outlier gene, typically within 10kb, gene expression can be regulated in part by 3D genomic conformation. Given increasing evidence that rare variants impact DNA architecture and 3D nuclear organization can influence the pathogenesis of rare disease, we hypothesize integrating genome topology will enable the identification of rare variants with functional activity that are more distal to the expression outlier gene. Our study aims to leverage Common Fund datasets to extend Watershed by utilizing 3D nucleome information from The 4D Nucleome (4DN) Program to prioritize causal rare variants in transcriptome data from The Undiagnosed Disease Network (UDN) and The Genotype-Tissue Expression (GTEx) Project. Our proposed project will integrate cell-type specific and expression-matched annotations from dilution Hi-C experiments in 4DN with RNA-Seq data from GTEx and UDN. We will assess rare variant enrichment in highly- connected loci near gene expression outliers and develop Watershed-3D to utilize 4DN annotations to prioritize impactful rare variants in healthy individuals and those with rare diseases. We will evaluate its performance using N2 pairs, an approach we have previously applied to both GTEx and UDN, where two or more individuals share the same rare variant and the predicted outlier score for one individual may be evaluated based on the observed outlier status of the other individual(s) sharing the variant. Our outcome will be whether 3D annotations improve rare variant detection compared with previous models that do not include genome topology annotations and prioritize impactful, long-range rare regulatory variants in UDN samples. We will provide the genomics community with annotations for rare variants in GTEx and UDN and an expanded Watershed-3D model in a reproducible, cloud-ready workflow. Combined, this proposal represents a significant opportunity to integrate our emerging understanding of nuclear topology and organization with genetic information to more systematically prioritize functional rare variant effects and their roles in health and disease.

NIH Research Projects · FY 2025 · 2025-06

Project Summary Hydrocephalus is a disease characterized by excess cerebrospinal fluid (CSF) accumulation in the brain, leading to elevated intracranial pressure (ICP). Current treatment options include the surgical implantation of a shunt that relieves pressure by draining the excess CSF. However, almost 100% of implanted shunts fail within 10 years, resulting in a life-threatening condition necessitating additional surgery. Diagnosing shunt failure is challenging due to non-specific symptoms and the lack of non-invasive shunt patency measurement, resulting in unnecessary hospital visits and potential for permanent neurologic injury or death. ICP monitoring remains one of the most definitive ways of diagnosing shunt failure, yet current methods are either highly invasive or lack reliability. Thus, there is a critical need for a technology that enables ICP monitoring from directly within the ventricles in an accurate, reliable and non-invasive manner. This project will develop a passive, shunt-mountable ICP sensor that can be wirelessly interrogated with ultrasound in a safe, comfortable and reliable manner. This project’s workflow is structured into three distinct yet interrelated aims. Mathematical models and finite element simulations will be used to optimize the design an ICP sensor with high sensitivity and dynamic range. The optimized designs will be fabricated, and their performance evaluated in benchtop experiments (aim 1). A pre-clinical ultrasound system will be used to localize and wirelessly communicate with the sensor through a skull phantom in water tank measurements (aim 2). Finally, the sensor electronics will be miniaturized, and packaging will be optimized for implantable applications. The system will be tested in a hydrocephalus rodent model and the results will be benchmarked against a commercial ICP sensor (aim 3). The innovation lies in the pioneering use of acoustic frequency combs to sense and communicate ICP through the skull, from a fully passive, implanted microscale sensor. Significantly, the proposal advances a fundamentally new tool that will enable longitudinal, non-invasive and accurate ICP monitoring at the clinic or bedside using ultrasound, thus eliminating the need for costly imaging, additional surgery, and patient distress. The ability to reliably monitor ICP also provides access to a wealth of previously unavailable biometric data that might have broader applicability for surveillance of brain tumor recurrence or for monitoring of traumatic brain injuries. The K99 phase will provide dedicated training and career growth opportunities in implantable sensor development, ultrasound imaging, bioelectronics, animal study design, small animal imaging and other dedicated preparation needed to transition to a faculty position and an independent research career developing sensors for continuous health monitoring. These training activities will provide the skills necessary to complete the proposed sensor miniaturization, packaging and animal study in the R00 phase.

NIH Research Projects · FY 2025 · 2025-06

PROJECT SUMMARY This proposal focuses on the molecular and cellular mechanisms of metastasis to the brain in small-cell lung cancer (SCLC), one of the most fatal forms of human cancer. Brain metastases are very frequent and a major cause of morbidity and mortality in SCLC patients. It has proven to be extremely challenging to investigate SCLC brain metastasis because surgery is very rarely performed to remove brain metastases in SCLC patients. There is also a dearth of pre-clinical models to study SCLC brain metastasis. In previous work using pre-clinical mouse models and intracranial injections of SCLC cells, we have found an upregulation of neuronal programs in SCLC cells during metastatic progression. Our preliminary data further indicate that this induction of neuronal programs endows SCLC cells with the ability to functionally interact with various cell types in the brain microenvironment, thereby promoting the ability of SCLC cells to grow in the brain. Mechanistically, this upregulation of neuronal programs is often due to the upregulation of the transcription factor NFIB, a known regulator of neuronal programs in the developing brain. My goal is to further investigate key cellular and molecular mechanisms of SCLC brain metastasis. I propose two Specific Aims. First, because ~15% of patients with SCLC have brain metastases at the time of their cancer diagnosis, it is important to investigate the mechanisms of SCLC growth in the brain. I will use intracranial injections of SCLC cells to model the growth of brain metastases in the brain of patients with SCLC. Using these cell models, I will perform in vivo CRISPR/Cas9 knockout screens focusing on candidates with known functions in neuronal biology during brain development. In parallel, I will perform similar screens in subcutaneous tumors to identify genes that are important for growth of SCLC specifically in the brain. A pilot screen has identified Leucine-rich repeat-containing 4B (LRRC4B, also known as Netrin-G ligand-3, NGL3), a synaptic adhesion molecule known for its involvement in excitatory synapse stability, to be important for SCLC growth in the brain. The role of LRRC4B will be investigated using gain- and loss-of-function studies in SCLC cell lines to understand its potential in facilitating interactions between SCLC cells and cells within the brain microenvironment. I will also use co-culture assays with murine neurons to investigate the role of LRRC4B in the functional interactions between SCLC cells and neurons. Second, because ~70% have brain metastases at the time of death from ~15% at the time of diagnosis, it is important to also investigate how new metastases are generated in the brain of patients with SCLC. I have identified human cell lines with brain tropism in mice. I will perform serial transplantation in the brain to enrich for brain metastasis phenotypes. I will also use these cell models to assess the importance of candidate genes, including NFIB, in the metastatic seeding of SCLC cells to the brain. The proposed experiments will uncover key genes and pathways that contribute to the striking ability of SCLC to metastasize to the brain and will identify mechanisms of SCLC brain metastasis that may serve as the basis to develop new strategies to treat SCLC patients with brain metastasis.

NSF Awards · FY 2025 · 2025-06

The space between the stars is not empty: interstellar space is filled with diffuse gas and dust, and threaded by invisible magnetic fields. This material is the interstellar medium (ISM), the stuff out of which new stars are born. The ISM is a wonderful physics laboratory. It is sculpted by a rich array of physical processes, and so observations of the ISM can be used to decipher the poorly understood physics that governs the formation of stars and the evolution of gas in galaxies. Astronomers observe the ISM in many wavelengths of light. This proposal will develop novel tools to unlock the physical information encoded in those observations. This work will generate a new understanding of the gas, dust, and magnetic fields in our Milky Way galaxy. Because the ISM obscures our view of light from the very early universe, this work will also help clear the way for cosmological discovery. This proposal will also build tight links between cutting-edge research and education, impacting students from high school through graduate school. This project will 1) develop a suite of pedagogical Jupyter notebooks to enable active learning in graduate ISM education, 2) host a workshop on integrating computational tools into the classroom, and 3) develop a new dual-enrollment astrophysics course focused on modern approaches to data-driven inference, serving low-income high school students. This proposal will develop cutting-edge methodologies for physical inference with complex data. The researchers will pursue a new approach to determining the phase structure of interstellar gas, and therefore to understanding the evolution of gas in galaxies. This work will open new avenues of inquiry by building computational tools to compare data and numerical simulations in high-dimensional spaces, and will reveal new insights into the magnetic structure of the ISM. This work will also build a new approach to modeling the structure of polarized dust emission from our Galaxy. This project also aims to realize the promise of the ISM for teaching and learning the universe. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-06

PROJECT SUMMARY/ ABSTRACT One in four of the 6 million U.S. older adults with Alzheimer’s and related dementias (ADRD) is hospitalized from the Emergency Department (ED) each year. Evidence shows 11% of these ED hospitalizations are potentially avoidable; however, few interventions have effectively reduced ED hospitalizations among older adults with ADRD. Geriatric Emergency Departments (GEDs) are an innovative care delivery intervention shown to reduce ED hospitalization rates. However, the limited understanding of how GEDs work across the U.S., what care processes are most effective, and how best to implement GEDs are critical barriers to the continued adoption of this promising new intervention. To overcome these barriers, we will link national Medicare claims to detailed GED accreditation data to determine specific care processes associated with avoidable hospitalizations and identify best practices for GED implementation. We have assembled a team of experts in in emergency medicine, geriatrics, implementation science, and organizational behavior and developed a strategic research-practice partnership with the GED Accreditation program that will allow us to study GEDs with unprecedented depth and impact. In Aim 1, we will identify GED care processes adopted at high-performing GEDs with the largest reductions in hospitalizations and increases in hospital-free days within 30 days among older adults with ADRD. Next, we will use qualitative methods to compare barriers, facilitators, and organizational factors associated with GED implementation among high-performing GEDs, low-performing GEDs, and non-GEDs (Aim 2). Finally, we will translate our findings into a toolkit of feasible and high impact implementation strategies in partnership with the GED Accreditation program (Aim 3). Successful completion of these aims will result in a comprehensive understanding of effective GED care processes and an actionable toolkit of strategies to improve GED implementation and reduce avoidable hospitalization among older adults with ADRD. Our work is impactful and innovative because we will translate our findings to real-world outcomes using a research-practice partnership; apply expertise in organizational change to accelerate innovation adoption; and use novel, rigorous causal inference and implementation science methods. This proposal directly advances NIA ADRD milestones 13N and 13P to elucidate the mechanisms of health care utilization and quality and evaluate the impact of policies on persons living with ADRD and responds to the Notice of Special Interest (NOT-AG-21-046) on programs and services for persons with dementia. Our findings will inform future GED guidelines and catalyze the evidence-based adoption of care delivery innovations to reduce avoidable hospitalizations for the millions of older adults with ADRD who experience ED visits annually.

NIH Research Projects · FY 2026 · 2025-06

Project Summary/Abstract Cannabis is the most widely used illicit drug among adolescents ages 14 -18 in the United States. Research demonstrates cannabis use is not harmless, especially given that the adolescent brain is particularly susceptible to dependence, and due to cannabis-related risks to the lungs, increased risk for depression, and decreased academic performance. Despite these health risks, since the mid-2000s, adolescents’ perceived risk of cannabis use has declined, and adolescents’ approval of cannabis use has increased. To date, very few comprehensive programs addressing adolescents’ misperceptions and knowledge about cannabis and preventing and reducing the use of all cannabis products have been developed, evaluated, or widely disseminated throughout the U.S. To address this gap, using a community-based participatory research approach in which we included a large group of adolescents and young adults, parents, educators, and healthcare providers with expertise in addiction medicine, we developed the “Smart Talk: Cannabis Prevention & Awareness” (Smart Talk) curriculum, which includes 5 lessons focused on health and environmental affects of cannabis, marketing, stress and coping, and refusal skills. Aligning with the NIH Stage Model for Behavioral Intervention Development, and through an NIH R34 grant in which we conducted a pilot randomized control trial with six schools, we have addressed 3 of the 6 stages needed to adequately develop, evaluate, refine, and fully implement and disseminate our Smart Talk curriculum. Our next step is to conduct a full evaluation to determine the real-world efficacy and effectiveness of the Curriculum (Stages III-IV), to determine for whom the Curriculum is most and least effective, and to further implement and disseminate the Curriculum (Stage V). As such, using a cluster-randomized trial, stepped-wedge design, with 30 middle and 30 high schools in California and New York (n=10,800 students), the Specific Aims of this proposed project are to: (1) Determine whether the Smart Talk Curriculum is effective in increasing adolescents’ knowledge of the different forms of cannabis and resistance to using and decreasing their positive attitudes towards and intentions to use cannabis products; (2) Determine whether the Smart Talk Curriculum is effective in changing adolescents’ actual use of different forms of cannabis (including preventing initiation, continuation, escalation; encouraging decreased use and cessation; and reducing co-use of cannabis and tobacco use); and (3) Examine the heterogenous treatment effects (HTE) of the intervention, identifying both those who benefit the most and those who do not benefit from the curriculum. The timing of this proposed research is extremely important given the rates of cannabis use in adolescence, legalization of cannabis across the country, and need for comprehensive cannabis education and prevention programs. Already we have hundreds of schools implementing Smart Talk, so determining the real-world effects of the curriculum and revising the curriculum as needed will further address the needs of schools and adolescents.